JP4545167B2 - Image processing method, image processing apparatus, image forming apparatus, computer program, and recording medium - Google Patents

Description

  The present invention relates to an image processing method, an image processing device, and an image processing device that perform image region determination (for example, a region including many small character regions, a character region on halftone dots, or a line region). The present invention relates to an image forming apparatus including the image processing apparatus, a computer program for realizing the image processing apparatus, and a recording medium on which the computer program is recorded.

  An image forming apparatus such as a copying machine or a printer using an electrophotographic process or an inkjet method can reproduce a color image with high image quality along with the progress of digital image processing technology. Multifunction devices have been commercialized. Images obtained from a document copied using such an image forming apparatus include characters, halftone photographs, line drawings, photographs (continuous gradation areas such as photographic paper photographs), or a mixture of them. In order to obtain a good reproduction image by copying a document, it is necessary to perform image processing suitable for each type of document.

  For example, the pixel or pixel block belonging to the character in the document, the pixel or pixel block belonging to the photograph, the pixel or pixel block belonging to the halftone dot is counted from the image signal obtained by reading the document, and the pixel or pixel of the entire document By calculating a ratio to the block and controlling at least one of the spatial filter process, the color correction process, the black generation process, the under color removal process, the halftone process, or the gamma correction process based on the calculated ratio An image processing apparatus that performs optimal processing according to the type of document has been proposed (see Patent Document 1).

  On the other hand, not all halftone dots but line lines (line screens) are used in some originals as gradation representations. For such originals, when edge detection is performed, all lines are displayed with fine lines. Since it is expressed, there is an edge, and there is a problem that it is impossible to accurately separate a character and a line.

  Therefore, for example, a mask of 5 × 5 pixels is set, the addition values P1 to P5 of the respective pixel values are calculated for each of the main scanning direction and the sub scanning direction, and the difference between the calculated addition values P1 to P5 is obtained. It is determined whether or not the density unevenness (shading) matches a predetermined pattern. Further, in order to distinguish from the character area, it is determined whether or not the difference between the “dark” portion of the density and the “light” portion of the density is smaller than a predetermined threshold, and the unevenness of the density matches the predetermined pattern. If the density difference is not too large, it is determined that the line is a line. If the pattern does not match, or if the density difference is large, it is determined that the line is not a line. An image processing apparatus that determines whether a document corresponds to a specific line pattern has been proposed (see Patent Document 2).

In addition, the image processing apparatus disclosed in Patent Document 2 uses, as a pixel of interest, a pixel that is determined not to be a line by the above-described determination processing, and, for example, four blocks are set around the pixel of interest. The number of pixels determined to be a line by the above determination process is counted. Then, the counted number of pixels is compared with a predetermined reference value, and if it is equal to or larger than the reference value, the determination result in the above-described determination process is corrected by setting this target pixel as a line.
Japanese Patent Laid-Open No. 7-298074 JP 2002-300388 A

  However, in the image processing apparatus disclosed in Patent Document 1, an area containing a lot of very small characters (for example, characters printed with a font size of about 4 points) or a complex halftone dot in a document. In an area that contains a lot of characters (for example, small characters printed on a background colored with complicated maps, catalog specifications, etc.), it is very difficult to determine that the type of document is a character, There was a high possibility that it was judged as an incorrect type, such as a halftone dot. Even though it is a text document, it is erroneously determined to be a halftone document, and for example, the filtering process may be smoothed, or the gradation process may be halftone processed. However, there is a problem that the sharpness of characters is lost and the image quality is deteriorated.

  Further, in the image processing apparatus of Patent Document 2, since there are various types of line patterns, many patterns are stored in advance when the determination is made based on whether the patterns match. In addition, by performing determination processing on many patterns, the determination processing becomes complicated and the amount of processing becomes enormous. Therefore, in consideration of feasibility, it is necessary to limit the types of detectable line patterns to specific ones by limiting the types of line patterns.

  The present invention has been made in view of such circumstances. The object of the present invention is to create various patterns of lines, small characters, characters on complex halftone dots, etc. (hereinafter also referred to as pseudo characters). ) Is accurately determined, and when pseudo characters are included, an image processing method, an image processing apparatus, and an image processing method that can prevent image quality deterioration by performing optimal image processing An object of the present invention is to provide an image forming apparatus including a processing apparatus, a computer program for realizing the image processing apparatus by a computer, and a recording medium on which the computer program is recorded.

  Another object of the present invention is to provide an image processing method, an image processing apparatus, and the image processing apparatus capable of accurately determining whether or not an image includes a line or a lowercase letter or a halftone dot character. An object is to provide an image forming apparatus.

  Another object of the present invention is to divide an image into a plurality of pixel blocks and perform region determination for each divided pixel block, thereby determining whether or not there is a pseudo character for each required region of the image. A processing apparatus and an image forming apparatus including the image processing apparatus are provided.

  Another object of the present invention is that a document from which an image is obtained includes a line area, a lower case area, or a halftone dot character area based on the number of pixel blocks determined to be the same type. An object of the present invention is to provide an image processing apparatus capable of accurately determining whether a document is an original, and an image forming apparatus including the image processing apparatus.

An image processing method according to the present invention is an image processing method for performing a process of determining the type of an image based on a pixel value of the pixel of an image of a document composed of a plurality of pixels. a first edge detecting step of detecting an edge pixel based on pixel values, on the basis of the detected edge pixel in the first edge detection step is the ratio of the number of edge pixels to the total number of pixels in the original entire image A first calculation step for calculating a first feature amount, a reduction step for reducing the image, and a second edge detection step for detecting an edge pixel based on a pixel value of each pixel of the image reduced in the reduction step; , based on the detected edge pixel in the second edge detecting step, calculating a second feature quantity is the ratio of the number of edge pixels to the total number of pixels of the reduced original entire image A plurality of directions, a ratio calculating step for calculating a ratio of the second feature amount calculated in the second calculation step with respect to the first feature amount calculated in the first calculation step, and a plurality of directions, A feature amount calculating step for calculating a feature amount related to the density distribution of the image, and determining whether or not there is an angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step. And a determination step of determining the type of the image based on the ratio calculated in the ratio calculation step and the determination result in the angle dependency determination step.

Image processing method according to the present invention, the first calculation step, a first counting step of counting the number of detected edge pixels in the first edge detection step, the edge pixels that are counted in the first counting step A first ratio with respect to the total number of pixels in the image, wherein the second calculating step counts the number of edge pixels detected in the second edge detecting step. And calculating a second ratio of the number of edge pixels counted in the second counting step to the total number of pixels in the image reduced in the reduction step.

In the image processing method according to the present invention, the determination step includes a determination step of determining whether the ratio calculated in the ratio calculation step is equal to or less than a predetermined threshold, and the ratio is determined to be a predetermined threshold in the determination step. And determining that the image includes a line when the determination result in the angle dependency determining step is “Yes”.
The image processing method according to the present invention is an image processing method for performing processing for determining a type of the image based on pixel values included in the pixel of the image of a document composed of a plurality of pixels, each of the image A first edge detecting step for detecting an edge pixel based on a pixel value of the pixel; a third calculating step for calculating a third feature amount that is the number of edge pixels of the document; a reducing step for reducing the image; A second edge detecting step for detecting an edge pixel based on the pixel value of each pixel of the image reduced in the reduction step, and a fourth calculation step for calculating a fourth feature amount that is the number of edge pixels of the reduced document. A second ratio calculating step for calculating a ratio of the fourth feature amount calculated in the fourth calculation step with respect to the third feature amount calculated in the third calculation step, and a plurality of directions. A feature amount calculating step for calculating a feature amount related to the density distribution of the image, and whether or not there is an angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step. An angle dependency determining step for determining, and a second determining step for determining the type of the image based on the ratio calculated in the second ratio calculating step and the determination result in the angle dependency determining step. It is characterized by.
In the image processing method according to the present invention, the second determination step includes a determination step of determining whether or not the ratio calculated in the second ratio calculation step is equal to or less than a predetermined threshold value, and the determination step. And determining that the image includes a line when the ratio is determined to be equal to or less than a predetermined threshold value and the determination result in the angle dependency determination step is present.

An image processing apparatus according to the present invention is an image processing apparatus that performs a process of determining the type of an image based on a pixel value of the pixel of an image of a document composed of a plurality of pixels. a first edge detecting means for detecting an edge pixel based on pixel values, on the basis of edge pixels first edge detecting unit detects, first is the ratio of the number of edge pixels to the total number of pixels in the original entire image A first calculating means for calculating one feature, a reducing means for reducing the image, a second edge detecting means for detecting an edge pixel based on a pixel value of each pixel of the image reduced by the reducing means, Second calculation means for calculating a second feature amount, which is a ratio of the number of edge pixels to the total number of pixels of the entire reduced image , based on the edge pixels detected by the second edge detection means; Calculation A ratio calculation unit that calculates a ratio of the second feature amount calculated by the second calculation unit with respect to the first feature amount calculated by, and a feature that calculates the feature amount related to the density distribution of the image for a plurality of directions, respectively. The amount calculation means, the angle dependency determination means for determining the presence or absence of the angle dependency of the density distribution of the image, and the ratio calculation means based on the feature amount in each direction calculated by the feature amount calculation means And a determination unit that determines the type of the image based on the ratio and the determination result of the angle dependency determination unit.

The image processing apparatus according to the present invention, the number of the first calculating means includes a first counting means for counting the number of edge pixels by the first edge detecting unit detects the edge pixels first counting means has counted Means for calculating a first ratio with respect to the total number of pixels in the image, and the second calculation means includes second counting means for counting the number of edge pixels detected by the second edge detection means; And a means for calculating a second ratio of the number of edge pixels counted by the second counting means to the total number of pixels in the image reduced by the reducing means.

  The image processing apparatus according to the present invention is configured such that the feature amount calculating unit calculates a sum of absolute values of differences between pixel values of adjacent pixels in each direction of the image, and the angle The dependency determining means is configured to determine whether or not there is angle dependency based on the sum calculated for each direction.

  In the image processing apparatus according to the present invention, the determination unit determines whether or not the ratio calculated by the ratio calculation unit is equal to or less than a predetermined threshold, and the ratio is equal to or less than the predetermined threshold by the determination unit. And a means for determining that the image includes a line when there is a determination result of the angle dependence determination means.

  In the image processing apparatus according to the present invention, when the determination unit determines that the ratio is equal to or less than a predetermined threshold by the determination unit, and the determination result of the angle dependency determination unit is none, lowercase letters or halftone dots It has a means to determine that it is an image containing the upper character.

  The image processing apparatus according to the present invention is characterized in that the determination means is configured to determine an image type for each pixel block including a plurality of pixels.

The image processing apparatus according to the present invention obtains the image on the basis of means for counting the number of pixel blocks determined to be the same type by the determination means, and the number of pixel blocks counted by the means. And means for discriminating the type of the original document.
The image processing apparatus according to the present invention is an image processing apparatus that performs processing for determining the type of the image based on pixel values included in the pixel of the image of the composed document of a plurality of pixels, each of the image First edge detection means for detecting edge pixels based on pixel values of pixels, third calculation means for calculating a third feature quantity that is the number of edge pixels of the document, reduction means for reducing the image, A second edge detecting means for detecting edge pixels based on pixel values of each pixel of the image reduced by the reducing means; a fourth calculating means for calculating a fourth feature quantity that is the number of edge pixels of the reduced document; A second ratio calculating unit that calculates a ratio of the fourth feature amount calculated by the fourth calculating unit to a third feature amount calculated by the third calculating unit; and a plurality of directions in the density distribution of the image. These feature quantities A feature amount calculating means for calculating, an angle dependency determining means for determining presence / absence of an angle dependency of the density distribution of the image based on the feature amount in each direction calculated by the feature amount calculating means, and the second ratio. And a second determination unit that determines the type of the image based on the ratio calculated by the calculation unit and the determination result of the angle dependency determination unit .
Further, the image processing apparatus according to the present invention is configured such that the feature amount calculating unit calculates the sum of absolute values of differences between pixel values of adjacent pixels for each direction of the image, The angle dependency determining means is configured to determine the presence or absence of angle dependency based on the sum calculated for each direction.
In the image processing apparatus according to the present invention, the second determination unit determines whether the ratio calculated by the second ratio calculation unit is equal to or less than a predetermined threshold, and the determination unit And a means for determining that the image includes a line when it is determined that the ratio is equal to or less than a predetermined threshold value and the determination result of the angle dependency determination means is present.
In the image processing apparatus according to the present invention, when the second determination unit determines that the ratio is equal to or less than a predetermined threshold by the determination unit, and the determination result of the angle dependency determination unit is none, It has a means to determine that it is an image containing a small letter or a character on a halftone dot.
Moreover, the image processing apparatus according to the present invention is characterized in that the second determination means is configured to determine the type of image for each pixel block composed of a plurality of pixels.
Further, the image processing apparatus according to the present invention is based on means for counting the number of pixel blocks determined to be the same type by the second determination means, and based on the number of pixel blocks counted by the means, And means for discriminating the type of document from which the image is obtained.

  An image forming apparatus according to the present invention includes any one of the above-described image processing apparatuses and an image forming unit that forms an output image based on an image processed by the image processing apparatus.

A computer program according to the present invention is a computer program for causing a computer to determine the type of an image based on a pixel value of the pixel of an image of an original composed of a plurality of pixels. A first edge detecting step for detecting an edge pixel based on a pixel value of each pixel; and an edge pixel corresponding to the total number of pixels of the image of the entire document based on the edge pixel detected by the computer at the first edge detecting step. a first calculation step of calculating a first characteristic quantity is the ratio of the number of the computer, a reduced step to shrink the image, to the computer, based on the pixel value of each pixel of the reduced image in said reduced step a second edge detecting step for detecting an edge pixel Te, the computer, the second edge detection stearate Based on the detected edge pixel in-flop, a second calculation step of calculating a second feature quantity is the ratio of the number of edge pixels to the total number of pixels of the reduced original entire image, to the computer, the first A ratio calculation step for calculating a ratio of the second feature quantity calculated in the second calculation step with respect to the first feature quantity calculated in the calculation step; and causing the computer to relate to the density distribution of the image in a plurality of directions. A feature amount calculating step for calculating each feature amount; and an angle dependency for causing the computer to determine whether or not there is an angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step. And determining the image based on the ratio calculated in the ratio calculation step and the determination result in the angle dependence determination step. Characterized in that it comprises a determining step of determining the type.
A computer program according to the present invention is a computer program for causing a computer to determine the type of the image based on a pixel value of the pixel of an image of an original composed of a plurality of pixels. A first edge detecting step of detecting edge pixels based on a pixel value of each pixel of the image; a third calculating step of causing a computer to calculate a third feature amount that is the number of edge pixels of the document; and A reduction step for reducing the image; a second edge detection step for causing the computer to detect an edge pixel based on a pixel value of each pixel of the image reduced in the reduction step; and an edge pixel of the reduced document in the computer. A fourth calculation step of calculating a fourth feature quantity that is a number of A second ratio calculating step for calculating a ratio of the fourth feature amount calculated in the fourth calculation step with respect to the third feature amount calculated in the step; and causing the computer to calculate the density distribution of the image in a plurality of directions. A feature amount calculating step for calculating the feature amount, and an angle dependency for causing the computer to determine whether or not there is an angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step. And a determination step for causing the computer to determine the type of the image based on the ratio calculated in the second ratio calculation step and the determination result in the angle dependency determination step. To do.

  A recording medium according to the present invention is recorded with the computer program described above.

According to the present invention, an edge pixel is detected based on the pixel value of each pixel of the image before reduction, and the first feature amount (third feature amount) is calculated based on the detected edge pixel . The image is reduced, an edge pixel is detected based on the pixel value of each pixel of the reduced image, and a second feature amount (fourth feature amount) is calculated based on the detected edge pixel . A ratio of the second feature value (fourth feature value) to the first feature value (third feature value) is calculated. In addition, a feature amount related to the density distribution of the image is calculated for a plurality of directions, and the presence / absence of angle dependency of the density distribution of the image is determined based on the calculated feature amount of each direction. The type of the image is determined based on the ratio of the second feature value (fourth feature value) to the first feature value (third feature value) described above and the presence or absence of the angle dependency.

According to the invention, as the first feature amount, a first ratio of the number of edge pixels detected based on a pixel value of each pixel of the image before the reduction to the total number of pixels of the image is calculated. As the second feature amount, a second ratio of the number of edge pixels detected based on the pixel value of each pixel of the reduced (or low resolution converted) image to the total number of pixels of the reduced image is calculated. To do. Then, the ratio of the second ratio to the first ratio is calculated.

Here, for example, when an image containing many small characters (for example, a font size of about 4 points) or an image containing complicated halftone dots is reduced, the distance between the characters is reduced and the characters are connected together. Or the shape of the character is broken, making it difficult to recognize the character. Further, when an image including a line is reduced, the lines are connected and integrated, making it difficult to recognize the line. That is, in an image including many small characters, complex halftone characters, or an image including a line (image before reduction), the second ratio (fourth feature ) compared to the first ratio (third feature amount). Amount) becomes very small.

On the other hand, for example, when an image including normally used characters (for example, font size is about 9 to 11 points) is reduced, even if the distance between the characters is reduced, the characters are rarely connected. Or, the character can be recognized as a character without being deformed. That is, in an image including characters that are normally used (image before reduction), the second ratio (fourth feature amount) is slightly smaller than the first ratio (third feature amount) , but many small characters are included. Compared to the case of an image, a character on a halftone dot, or an image including a line, the rate of decrease in the ratio is small. Accordingly, it is possible to determine whether or not a pseudo character is included in the image based on the ratio of the second ratio (fourth feature quantity) to the calculated first ratio (third feature quantity) .

  In the present invention, when many small characters are included, it is expressed that “lowercase” is included or a lowercase area is included. Also, including many small characters means, for example, that there are multiple lines of text consisting of characters with a font size of 4 points or 5 points, or an area written in the above size in an area of about 3 cm × 3 cm. The case where it exists. Whether or not the small character area can be determined depends on the performance of an image forming apparatus such as a scanner or a digital copying machine. Therefore, the range of the area including the small character (lower limit) according to the performance of these apparatuses. You can decide.

In addition, since each line is composed of a plurality of parallel straight lines, and the plurality of straight lines have an angle dependency, by comparing feature amounts in a plurality of directions, a plurality of lines can be obtained from the angle dependency of the density distribution. The angle dependence of the straight line can be obtained. When a plurality of straight lines have an angle dependency, it can be accurately determined that an image including the straight lines is an image including a parallel line. Since it is only necessary to calculate the feature amount related to the density distribution in each direction, it is possible to easily determine the line. Accordingly, the angle dependency of the density distribution of the image with respect to the image determined to contain the pseudo character based on the ratio of the second ratio (fourth feature quantity) to the first ratio (third feature quantity) described above. It is possible to determine whether or not the image includes a line of pseudo characters based on the presence or absence of.

  In the present invention, the fact that the density distribution or the edge has an angle dependency means that the feature amount related to the density distribution in a specific direction (for example, the direction of 0 degree (main scanning direction)) is a large value. Alternatively, it means that the appearance frequency of the edge increases, and that there is no density distribution or edge angle dependency means that the density distribution or the edge does not satisfy the above condition.

  According to the present invention, the sum of absolute values of differences in density values between adjacent pixels is calculated for a plurality of directions of an image. This sum is a feature amount related to the density distribution of the image. The sum is large in the direction where the density change is large, and the sum is small in the direction where the density is small. Since the direction in which the density change is small is the direction of a plurality of straight lines constituting the line, whether or not the line is included can be determined by examining the presence or absence of the angle dependency of the sum. Since the determination can be performed only by addition, subtraction, and comparison, the processing is simple.

According to the present invention, the ratio of the second ratio (fourth feature amount) based on the image after reduction to the first ratio (third feature amount) based on the image before reduction is equal to or less than a predetermined threshold, and a plurality of If the feature amount related to the density distribution of the image calculated with respect to the direction has angle dependency, it is determined that the image includes a line. Further, when the ratio is equal to or less than a predetermined threshold value and the feature amount related to the density distribution of the image calculated for a plurality of directions has no angle dependency, it is determined that the image includes lowercase letters or halftone characters. Therefore, it is possible to accurately determine whether the image includes a line, a lowercase letter, or a halftone dot character.

  In addition, when it is determined that the image contains lines, lowercase letters, or halftone dots, a determination signal is output to perform optimum image processing according to the type of the image and to have high quality. An image can be formed. Specifically, for example, in the filter process, a filter that is smoother than an image including a line is processed by a filter used for a character / printed photo document in which characters and a printed photo are mixed. For images containing many small characters using a coefficient, a filter coefficient is used that makes the characters slightly sharper than when processing with a filter used for a character / printed photographic document. In the halftone processing (gradation reproduction processing unit), dither processing or error diffusion processing that emphasizes gradation is performed on an image including a line, and for an image including many small characters, Performs processing that emphasizes resolution.

According to the present invention, an image is divided into a plurality of pixel blocks (for example, 4 × 4 pixels, 8 × 8 pixels, etc.), and the first ratio (third feature amount) calculated for each divided pixel block is used. Based on the ratio of the second ratio (fourth feature amount) and the feature amount related to the density distribution, an area determination is performed as to whether or not each pixel block includes a line, a lower case character, or a halftone dot character. Thereby, the presence or absence of a pseudo character can be determined for each required area of the image.

  According to the present invention, it is determined whether each divided pixel block includes a line or a lowercase letter or halftone dot character, and a line or a lowercase letter or halftone dot character is included. Based on the number of pixel blocks determined to be, it is determined that the original from which the image was obtained includes a line area, a lowercase area, or a character area on halftone dots. As a result, it is possible to accurately determine whether the original includes a line area, a lower case area, or a halftone character area. Specifically, when the determined number of pixel blocks is larger than a predetermined threshold, it is determined that the original from which the image is obtained includes a line area, a lowercase area, or a halftone dot character area.

  According to the present invention, the above-described computer program is recorded on a recording medium. The computer can read out the computer program from the recording medium and perform a process of determining whether the image includes a line, a lowercase letter, or a halftone dot character.

In the present invention, an edge pixel is detected based on an image before reduction, a first feature amount (third feature amount) is calculated based on the detected edge pixel , and the image is reduced and reduced. An edge pixel is detected based on the detected image, and a second feature amount (fourth feature amount) is calculated based on the detected edge pixel . Whether the image includes pseudo characters (lines, lowercase letters, or halftone dots) based on the ratio of the second feature value (fourth feature value) to the calculated first feature value (third feature value) By determining whether or not there is a pseudo character, it is possible to accurately determine the presence or absence of a pseudo character. In addition, the feature amount related to the density distribution of the image is calculated for a plurality of directions, and based on the presence / absence of the angle dependency of the density distribution, it is determined whether or not the image includes a line. Presence / absence can be accurately determined. Furthermore, optimal image processing can be performed on an image determined to contain pseudo characters, and image quality deterioration can be prevented and image quality can be improved when image formation is performed.

  In the present invention, the first ratio based on the image before reduction is calculated as the first feature amount, and the second ratio based on the image after reduction is calculated as the second feature amount. Based on the ratio of the second ratio to the first ratio, it is determined whether or not the image includes a pseudo character by simply determining whether the image includes the pseudo character or not. Can be determined. Further, since it can be accurately determined that the image includes pseudo characters, it is possible to reliably perform optimal image processing on the image including pseudo characters, and to reliably improve image quality.

  Furthermore, in the present invention, the sum of absolute values of differences in density values between adjacent pixels is calculated as a feature amount for a plurality of directions of an image, and the presence / absence of angle dependency is determined based on this sum. With this configuration, it is possible to easily determine whether or not the image includes a line by simply calculating the sum. In addition, since it can be accurately determined that the image includes a line, optimal image processing can be reliably performed on the image including a line, and the image quality can be reliably improved.

In the present invention, the ratio of the second ratio (fourth feature amount) based on the image after reduction to the first ratio (third feature amount) based on the image before reduction is equal to or less than a predetermined threshold, and If the feature amount related to the density distribution of the image calculated for the direction of the image has angle dependency, it is determined that the image includes a line, the ratio is equal to or less than a predetermined threshold value, and the image is calculated for a plurality of directions. When the feature amount related to the density distribution of the image region has no angle dependency, it is determined that the image includes lowercase letters or halftone dots. Therefore, it is possible to accurately determine whether the image includes a line, a lowercase letter, or a halftone dot character.

  In the present invention, an image is divided into a plurality of pixel blocks (for example, 4 × 4 pixels, 8 × 8 pixels, etc.), and each divided pixel block has a line, lowercase letters or halftone dots. The area is determined whether or not it is included. Accordingly, it is possible to determine the presence or absence of lines, lowercase letters or halftone dots for each required area of the image.

  In the present invention, the line area is added to the original from which the image is obtained based on the number of pixel blocks determined to be of the same type (line or area including lowercase letters or halftone dots). Alternatively, it is determined that a lowercase character area or a halftone dot character area is included. As a result, it is possible to accurately determine whether the original includes a line area, a lower case area, or a halftone character area.

  In the present invention, the computer program is recorded on the recording medium so that the computer reads the computer program from the recording medium, and the image includes lines, lowercase letters or halftone dots. Therefore, it is possible to easily perform a function of performing image processing optimal for each image.

(Embodiment 1)
Hereinafter, the present invention will be described in detail with reference to the drawings illustrating embodiments thereof. FIG. 1 is a block diagram illustrating a configuration of an image forming apparatus 100 including an image processing apparatus according to the present invention. The image forming apparatus 100 (for example, a digital color copying machine or a multi-function machine having a multi-function, a printer function, a fax function, an e-mail delivery function, etc.) includes a color image input device 1 and a color image processing device 2 (image processing). Apparatus), a color image output device 3 as image forming means, an operation panel 4 for performing various operations, and the like. Image data of RGB (R: red, G: green, B: blue) analog signals obtained by reading a document with the color image input device 1 is output to the color image processing device 2, and the color image processing device 2. Then, a predetermined process is performed and output to the color image output device 3 as a digital color signal of CMYK (C: cyan, M: magenta, Y: yellow, K: black).

  The color image input device 1 is, for example, a scanner including a CCD (Charged Coupled Device), reads a reflected light image from a document image as an RGB analog signal, and outputs the read RGB signal to the color image processing device 2. . The color image output device 3 is an image forming unit using an electrophotographic system or an inkjet system that outputs image data of a document image onto a recording sheet. The color image output device 3 may be a display device such as a display.

  The color image processing apparatus 2 includes an A / D conversion unit 20, a shading correction unit 21, a document type determination unit 22, an input tone correction unit 23, a region separation processing unit 24, a color correction unit 25, and a black generation and under color removal unit 26. , A spatial filter processing unit 27, an output tone correction unit 28, a tone reproduction processing unit 29, and a CPU (Central Processing Unit) or ASIC (Application Specific Integrated Circuit) that controls the operation of each of these hardware units. .

  The A / D conversion unit 20 converts the RGB signal input from the color image input device 1 into, for example, a 10-bit digital signal, and outputs the converted RGB signal to the shading correction unit 21.

  The shading correction unit 21 performs correction processing for removing various distortions generated in the illumination system, imaging system, imaging system, and the like of the color image input apparatus 1 on the input RGB signal. The shading correction unit 21 adjusts the color balance, and outputs the adjusted RGB signal to the document type determination unit 22.

  The original type discriminating unit 22 is used in the color image processing apparatus 2 such as a density signal using an RGB signal (RGB reflectance signal) from which various distortions have been removed by the shading correction unit 21 and the color balance has been adjusted. The signal is converted into an easy-to-handle signal for the image processing system, and the document type is determined. The document type determination unit 22 outputs a document type determination signal, which is a determination result of the document type, to an input tone correction unit 23, a color correction unit 25, a black generation and under color removal unit 26, a spatial filter processing unit 27, and a tone reproduction processing unit. To 29. The document type determination will be described later. The document type determination unit 22 outputs the RGB signal input from the shading correction unit 21 to the input tone correction unit 23 as it is.

  The input tone correction unit 23 performs a process of adjusting the color balance on the input RGB signal (RGB reflectance signal), and performs an image quality adjustment process such as removal of background density or contrast, and the processed RGB signal The signal is output to the region separation processing unit 24.

  Based on the input RGB signal, the region separation processing unit 24 separates whether each pixel in the input image is a character region, a halftone dot region, or a photographic region. Based on the separation result, the region separation processing unit 24 outputs a region identification signal indicating which region each pixel belongs to, a color correction unit 25, a black generation and under color removal unit 26, a spatial filter processing unit 27, a tone reproduction The data is output to the processing unit 29. The region separation processing unit 24 outputs the input RGB signal as it is to the subsequent color correction unit 25.

  The color correction unit 25 converts the input RGB signal into a CMY color space, performs color correction in accordance with the characteristics of the color image output device 3, and outputs the corrected CMY signal to the black generation and under color removal unit 26. To do. Specifically, the color correction unit 25 performs a process of removing color turbidity based on the spectral characteristics of CMY color materials including unnecessary absorption components in order to make color reproduction faithful.

  The black generation and under color removal unit 26 generates a K (black) signal based on the CMY signal input from the color correction unit 25 and generates a new CMY signal by subtracting the K signal from the input CMY signal. Then, the generated CMYK signal is output to the spatial filter processing unit 27.

  An example of processing in the black generation and under color removal unit 26 is shown. For example, in the process of generating black by skeleton black, the input / output characteristic of the skeleton curve is y = f (x), the input data is C, M, Y, and the output data is C ′, M ′. , Y ′, K ′, and a UCR (Under Color Removal) rate α (0 <α <1), the data output by the black generation and under color removal processing is K ′ = f {min (C, M, Y)}, C ′ = C−αK ′, M ′ = M−αK ′, and Y ′ = Y−αK ′.

  The spatial filter processing unit 27 performs spatial filter processing on the CMYK signal input from the black generation and under color removal unit 26 using a digital filter based on the region identification signal. As a result, the spatial frequency characteristics of the image data are corrected, and blurring of the output image in the color image output device 3 or deterioration of graininess is prevented. For example, the spatial filter processing unit 27 performs sharp enhancement processing on the region separated into character regions by the region separation processing unit 24 to enhance the reproducibility of black characters or color characters, and emphasizes high frequency components. The spatial filter processing unit 27 performs low-pass filter processing for removing the input halftone component on the region separated into the halftone dot region by the region separation processing unit 24. The spatial filter processing unit 27 outputs the processed CMYK signal to the output tone correction unit 28.

  The output tone correction unit 28 performs an output tone correction process for converting the CMYK signal input from the spatial filter processing unit 27 into a halftone dot area ratio that is a characteristic value of the color image output device 3, and The CMYK signal after the tone correction processing is output to the gradation reproduction processing unit 29.

  The gradation reproduction processing unit 29 performs predetermined processing on the CMYK signal input from the output gradation correction unit 28 based on the region identification signal input from the region separation processing unit 24. For example, the tone reproduction processing unit 29 is suitable for reproducing high-frequency components in the color image output apparatus 3 in order to improve the reproducibility of the black character or the color character in the region separated into the character region by the region separation processing unit 24. A binarization process or a multi-level process is performed.

  In addition, the gradation reproduction processing unit 29 performs gradation reproduction processing so that the region separated by the halftone dot region in the region separation processing unit 24 can be finally separated into pixels and the respective gradations can be reproduced. (Generate halftone). Further, the gradation reproduction processing unit 29 performs binarization processing or multi-value quantization processing on the region separated into the photographic region by the region separation processing unit 24 so as to be suitable for gradation reproducibility in the color image output device 3. .

  The color image processing apparatus 2 temporarily stores the image data (CMYK signal) processed by the gradation reproduction processing unit 29 in a storage unit (not shown), and stores the image data in the storage unit at a predetermined timing for image formation. And the read image data is output to the color image output device 3.

  The operation panel 4 includes, for example, a display unit such as a liquid crystal display and a setting button. The color image input device 1, the color image processing device 2, and the color image output device 3 are based on information input from the operation panel 4. Operation is controlled.

  FIG. 2 is a block diagram illustrating a configuration of the document type determination unit 22. The document type determination unit 22 includes a signal conversion unit 2201, an interpolation calculation unit 2202, a minimum density value calculation unit 2203, a maximum density value calculation unit 2204, an inversion number calculation unit 2205, an edge detection unit 2206, a maximum density difference calculation unit 2207, and a maximum. Density difference determination unit 2208, inversion number determination unit 2209, comprehensive determination unit 2210, edge detection result calculation unit 2211, memory 2212, character pixel block counting unit 2213, halftone pixel block counting unit 2214, other pixel block counting unit 2215, An edge count ratio determination unit 2216, a document determination unit 2217, a total density busyness calculation unit 2218, and the like are provided.

  The document type discriminating unit 22 performs preliminary scanning (pre-scanning) on the document, and in the sub-scanning direction (direction parallel to the scanning direction of the scanner) at a speed such as twice or four times the main scanning (main scanning). Scan and generate a reduced image by performing an interpolation operation in the main scanning direction (a direction perpendicular to the scanning direction of the scanner), and the generated reduced image is temporarily stored in an image memory (not shown) for subsequent processing. ). The document type determination unit 22 divides the reduced image into predetermined pixel blocks (for example, 4 × 4 pixels), and performs area determination such as a character area, a dot area, and other areas for each divided pixel block. The type of document is determined.

  Further, the document type discrimination unit 22 detects the edge of the reduced image, detects the ratio of the number of edges to the total number of pixels of the reduced image, and detects the edge of the image obtained by performing main scanning on the document, thereby obtaining the total image. Based on the ratio of the number of edges to the number of pixels, whether or not the region includes pseudo characters (for example, a character whose font size is as small as about 4 points, a character on a complex halftone dot, or a line) By performing the area determination, it is determined whether or not the original includes pseudo characters.

  Further, the document type determination unit 22 calculates a feature amount (total density busyness) related to the density distribution of an image obtained by performing main scanning on the document in an area including pseudo characters, and the calculated total density busyness is Based on whether or not there is an angle dependency, it is determined whether or not the document includes a line by determining whether or not the area includes a line. As described above, when the document type determination unit 22 determines that the region includes a pseudo character, the document type determination unit 22 determines whether the region includes a line of pseudo characters. Based on this, a document type discrimination signal as a final discrimination result is output.

The signal conversion unit 2201 converts the RGB signal (RGB reflectance signal) input from the shading correction unit 21 into a luminance signal (L signal) during preliminary scanning and main scanning, and performs an interpolation operation on the converted luminance signal. Output to the unit 2202 and the total density busyness calculation unit 2218. For example, the luminance signal Lj of each pixel can be expressed as Lj = 0.30 × Rj + 0.59 × Gj + 0.11 × Bj, where Rj, Gj, and Bj are the color components of each pixel RGB. Note that the RGB signal is not limited to the above formula, and can be converted into a CIE1976L ^* a ^* b ^* signal. Further, the signal conversion unit 2201 may output the luminance signal to the total density busyness calculation unit 2218 only during either the preliminary scan or the main scan.

  An interpolation calculation unit (reduction means) 2202 performs interpolation calculation by thinning out pixels from image data in the main scanning direction during preliminary scanning, and minimizes image data after the interpolation calculation (that is, image data of a reduced image). The density value calculation unit 2203, the maximum density value calculation unit 2204, the inversion number calculation unit 2205, and the edge detection unit 2206 are output. Further, the interpolation calculation unit 2202 outputs the input image data (that is, image data of an unreduced image) to the edge detection unit 2206 without performing the interpolation calculation during main scanning.

  The minimum density value calculation unit 2203 calculates the minimum density value in the pixel block of the reduced image input from the interpolation calculation unit 2202 and outputs the calculated minimum density value to the maximum density difference calculation unit 2207. The maximum density value calculation unit 2204 calculates the maximum density value in the pixel block of the reduced image input from the interpolation calculation unit 2202 and outputs the calculated maximum density value to the maximum density difference calculation unit 2207.

  The maximum density difference calculation unit 2207 calculates a difference (maximum density difference) between the minimum density value and the maximum density value input from the minimum density value calculation unit 2203 and the maximum density value calculation unit 2204, and the calculated maximum density difference is maximized. Output to the density difference determination unit 2208.

  The maximum density difference determination unit 2208 performs threshold processing on the maximum density difference input from the maximum density difference calculation unit 2207. If the maximum density difference is larger than the threshold value THsub, the pixel block is converted into a character area or a dot area (determination signal “ If the maximum density difference is equal to or smaller than the threshold value THsub, the pixel block is determined as another region (determination signal “1”), and the determination signal is output to the comprehensive determination unit 2210.

  The inversion number calculation unit 2205 obtains an average value of each pixel value in the pixel block of the reduced image input from the interpolation calculation unit 2202 and binarizes each pixel value using the average value. The inversion number calculation unit 2205 scans the binarized data in the pixel block, calculates the inversion number Kh in the main scanning direction and the inversion number Kv in the sub scanning direction, and calculates the inversion number Kh and the sub number in the main scanning direction. The inversion number Kv in the scanning direction is output to the inversion number determination unit 2209.

  The inversion number determination unit 2209 determines that the pixel block is a halftone area (determination signal “1”) when the inversion number Kh ≧ the predetermined threshold value THdt and the inversion number Kv ≧ the predetermined threshold value THdt, and the inversion number Kh. If <predetermined threshold THdt or inversion count Kv <predetermined threshold THdt, the pixel block is determined to be a character region (determination signal “0”), and a determination signal is output to the overall determination unit 2210.

  Based on the determination signal input from the maximum density difference determination unit 2208 and the determination signal input from the inversion number determination unit 2209, the comprehensive determination unit 2210 determines which region (for example, a character region, a dot region, It is finally determined whether the area is other area. The overall determination unit 2210 outputs a determination signal to any one of the character pixel block counting unit 2213, the halftone pixel block counting unit 2214, and the other pixel block counting unit 2215 according to the final determination result. For example, when the pixel block is finally determined to be a character region, the determination signal “1” is output to the character pixel block counting unit 2213, and when the pixel block is finally determined to be a halftone dot region, the determination signal “ 1 ”is output to the halftone pixel block counting unit 2214, and when it is finally determined that the pixel block is in another region, the determination signal“ 1 ”is output to the other pixel block counting unit 2215.

  FIG. 3 is an explanatory diagram illustrating a final determination method of the comprehensive determination unit 2210. As shown in the figure, when the determination signal from the maximum density difference determination unit 2208 is “0” and the determination signal from the inversion number determination unit 2209 is “1”, the comprehensive determination unit 2210 displays the pixel block as a network. When it is finally determined that the region is a point region, and the determination signal from the maximum density difference determination unit 2208 is “0” and the determination signal from the inversion number determination unit 2209 is “0”, the pixel block is a character region. When the determination signal from the maximum density difference determination unit 2208 is “1”, the pixel block is in other cases regardless of whether the determination signal from the inversion number determination unit 2209 is “1” or “0”. It is finally determined that this is the area.

  Note that the threshold used for determining the area of the pixel block, specifically, the threshold THsub for the maximum density difference and the threshold THdt for the number of inversions set the size of the pixel block, determine the area using various image samples, What is necessary is just to set the value isolate | separated to a character area | region, a dot area | region, and another area | region accurately. It is also possible to store the determination results of a plurality of pixel blocks and correct the determination results with reference to the determination results of surrounding pixel blocks.

  The character pixel block counting unit 2213 counts the determination signal “1” output from the comprehensive determination unit 2210, calculates the ratio of the count value of the determination signal to the total number of pixel blocks of the reduced image, and calculates the calculated ratio as the document determination unit. To 2217. The halftone pixel block counting unit 2214 counts the determination signal “1” output from the comprehensive determination unit 2210, calculates the ratio of the count value of the determination signal to the total number of pixel blocks of the reduced image, and determines the calculated ratio as a document determination Output to the unit 2217. The other pixel block counting unit 2215 counts the determination signal “1” output from the comprehensive determination unit 2210, calculates the ratio of the count value of the determination signal to the total number of pixel blocks of the reduced image, and determines the calculated ratio as a document determination Output to the unit 2217.

  The edge detection unit (second edge detection unit) 2206 scans each pixel in the pixel block of the post-interpolation image (that is, the reduced image) output from the interpolation calculation unit 2202 as a target pixel during preliminary scanning. The edge is detected by comparing the density difference between the target pixel and the neighboring pixels adjacent to the target pixel with the threshold value THed, and the edge image detected for each pixel block is output to the edge detection result calculation unit 2211. In addition, the edge detection unit (first edge detection unit) 2206 scans each pixel in the pixel block of the image (that is, an unreduced image) input from the interpolation calculation unit 2202 as the target pixel during main scanning. The edge is detected by comparing the density difference between the target pixel and the neighboring pixels adjacent to the target pixel with the threshold value THed, and the edge image detected for each pixel block is output to the edge detection result calculation unit 2211.

  FIG. 4 is an explanatory diagram showing an example of an edge detection method. As shown in the figure, the density difference between the target pixel and the neighboring pixels adjacent to the target pixel is obtained for the adjacent pixel in the main scanning direction and the adjacent pixel in the sub-scanning direction. Is represented by 256 gradations, it is determined that it is an edge.

  Note that the edge detection method shown in FIG. 4 is an example, and the edge detection method is not limited to this. For example, by performing a convolution integral calculation with image data using an edge detection filter such as a Sobel filter or a Laplacian filter, and performing a threshold process on the calculation result (determining an edge when the calculation result is equal to or greater than the threshold), An edge may be detected.

  An edge detection result calculation unit (second counting unit, second calculation unit) 2211 is based on the edge image input from the edge detection unit 2206 at the time of preliminary scanning, and edge pixels with respect to the total number of pixels of the entire reduced document image. The ratio Cs of the number of edges (number of edges) is calculated, and the calculated ratio Cs is stored in the memory 2212. The edge detection result calculation unit (first counting unit, first calculation unit) 2211 is based on the edge image input from the edge detection unit 2206 during the main scanning, and is the total number of pixels of the entire original image that has not been reduced. The ratio Co of the number of edge pixels (number of edges) with respect to is calculated, and the calculated ratio Co is output to the edge count ratio determination unit 2216.

  The edge count ratio determination unit (ratio calculation means) 2216 reads the ratio Cs stored in the memory 2212, obtains the ratio (Cs / Co) of the ratio Cs to the ratio Co input from the edge detection result calculation unit 2211, (Cs / Co) is compared with a threshold value THtc. When the ratio (Cs / Co) ≦ threshold value THtc, the edge count ratio determination unit 2216 includes pseudo characters (for example, small characters with a font size of about 4 points, complex halftone characters, or lines). ) Is included. Further, when the ratio (Cs / Co)> threshold value THtc, the edge count ratio determination unit 2216 determines that the document does not include pseudo characters, and outputs each determination result to the document determination unit 2217.

  A total density busyness calculation unit (feature amount calculation unit, angle dependency determination unit) 2218 scans each pixel of an image (an unreduced image) input from the signal conversion unit 2201 as a target pixel during main scanning. The total density busyness is calculated, and it is determined whether or not the calculated total density busyness has angle dependency. In addition, a plurality of parallel straight lines constituting a line have direction dependency, and by determining the presence or absence of the direction dependency of the straight line within the predetermined region, the presence or absence of angle dependency is determined, and the predetermined region It can be determined whether or not is a line region.

  FIG. 5 is a schematic diagram for explaining a method for determining the line region. The total density busyness calculation unit 2218, for example, for each 7 × 7 square region (hereinafter referred to as 7 × 7 region) including the target pixel (shown with hatching in FIG. 5). A plurality of straight lines that are parallel to each other exist, and it is determined whether or not the target pixel is a pixel in the line region by determining whether or not the plurality of straight lines have direction dependency. It is.

  A plurality of parallel straight lines exist in the 7 × 7 region, and whether or not the plurality of straight lines have direction dependency is determined by, for example, 0 ° (see FIG. 5A), 90 ° (see FIG. 5 (B)), 45 ° (see FIG. 5C), and 135 ° (see FIG. 5D), the absolute value of the difference in pixel value (density) between adjacent pixels. The total sum (total density busyness) is calculated and compared with a predetermined threshold value.

  Here, when there is angle dependency with respect to any one of the four directions, the value of the total density busyness is small only in a specific direction, and the value of the total density busyness is large in the other directions. Become. Therefore, the total density busyness calculation unit 2218 compares the calculated four total density busyness with a predetermined threshold value, and when only the total density busyness in a specific direction is smaller than the threshold value, It can be determined that the pixel is included in the line region.

  The total density busyness calculation unit 2218 determines whether each image block is a region including a line by repeating this processing for all pixels of the image block of the image input from the signal conversion unit 2201. To do. The total density busyness calculation unit 2218 repeats this process for all the pixel blocks of the image, and calculates the ratio of the number of pixel blocks determined to be a region including a line to the total number of pixel blocks of the image. The calculated ratio is compared with a predetermined threshold value. When the calculated ratio is greater than or equal to the threshold, the total density busyness calculation unit 2218 determines that the document includes a line, and when the calculated ratio is less than the threshold, determines that the document does not include a line. Each determination result is output to the document determination unit 2217. Each threshold may be a value that can accurately determine the edge angle dependency in various document images.

  The total density busyness calculation unit 2218 further calculates a difference between the total density busyness of 0 ° and 90 ° and a difference between the total density busyness of 45 ° and 135 °, and any one of the two differences is calculated. If it is larger than the threshold, it is possible to determine that this pixel of interest is a pixel included in the line region. The total density busyness calculation unit 2218 calculates the total density busyness during the main scan, the preliminary scan, or both, and each pixel is included in the line area based on the calculated total density busyness. What is necessary is just to determine whether it is a pixel. In addition, the total density busyness calculation unit 2218 determines not only the presence / absence of the angle dependency of the total density busyness in a predetermined area (for example, a 7 × 7 area) but also a difference between the maximum density value and the minimum density value in the predetermined area. It may be determined whether or not there is an angle dependency of a certain maximum density difference.

  In FIG. 2, a document determination unit (determination unit) 2217 includes a ratio of pixel blocks input from a character pixel block counting unit 2213, a halftone pixel block counting unit 2214, and other pixel block counting units 2215 and respective threshold values. And the type (type) of the entire document is determined. For example, if the ratio of the character pixel block and the ratio of the halftone pixel block are each equal to or greater than the threshold value, it is determined that the character / halftone original (character-printed photo original). The threshold value can be set to an appropriate value by setting the size of the pixel block to be processed and evaluating the various image samples using the set pixel block. Note that the document type determination signal, which is the result of determining the document type, is held without being output when the preliminary scanning is completed.

  The document determination unit 2217 needs the document type determination signal held based on the determination results input from the edge count ratio determination unit 2216 and the total density busyness calculation unit 2218 as a result of the preliminary scan and the main scan. The document type determination signal after the change is output. FIG. 6 is an explanatory diagram showing a document determination method of the document determination unit 2217. As shown in the figure, the document determination unit 2217 determines, for example, that characters are included in the document in preliminary scanning (for example, a character document, a character / print photo document, a character / print paper photo document, etc.) Based on the determination result from the edge count ratio determination unit 2216, when it is determined that the document contains pseudo characters (a line or a small character whose font size is about 4 points), the total density busyness calculation unit Based on the determination result from 2218, it is determined whether or not the total density busyness of the document has angle dependency.

  If the total density busyness of the document has an angle dependency, the document determination unit 2217 changes the document type determination signal to a document including a line, and the total density busyness of the document has an angle dependency. If not, the original type discrimination signal is changed and output to an original containing many small characters. The document determination unit 2217 determines that the document does not include characters in the preliminary scan, or determines that the document includes characters in the preliminary scan, and determines from the edge count ratio determination unit 2216. If it is determined that the original does not contain pseudo characters based on the result, the original type determination signal at the time of preliminary scanning is output as it is.

  As described above, the document type determination unit 22 according to the first embodiment separates a pixel block composed of a plurality of pixels into each region of characters, halftone dots, and other (for example, continuous tone regions such as photographs). For example, the maximum density difference and the total density busyness of each pixel block centered on the target pixel are calculated, and characters, halftone dots, and photographic paper photographs are calculated for each target pixel. Alternatively, the document type may be determined separately in the background area. In this case, the edge detection processing by the edge detection unit 2206 is performed for each pixel. Further, for each pixel, whether or not the edge has directionality may be determined based on whether or not the edge has angle dependency by using the total density busyness described above.

  Next, a method for discriminating pseudo characters (for example, a region containing many small characters, characters on complex halftone dots, lines, etc.) included in the document will be described. Here, the area containing many small characters is, for example, a case where characters having a font size of about 4 points are printed in a document (a list of catalog specifications, etc.), and a complex network. A dot character is, for example, a case where small characters are printed on a background colored with a complicated map, catalog specification, etc. in a manuscript. This is the case where a pattern composed of parallel lines with a narrow interval is printed in the document to represent the density of the image.

  FIG. 7 is a schematic diagram illustrating an example of an image of a document on which characters are printed, and FIG. 8 is a schematic diagram illustrating an example of an image of a document on which characters are printed in a line pattern. FIG. 7A shows, for example, an image A1 of a document in which characters “ABCDEFGH” (non-small characters) are printed in four rows on an A4 size paper with appropriate intervals, and FIG. FIG. 7A shows an original image A2 obtained by reducing the original (image) of FIG. 7A to a size of about 20%.

  Edges are detected for each of the images A1 and A2. In the case of the image A1, since there are many characters in the entire document, the number of detected edge pixels (characters) increases. The ratio Co of edge pixels to the total number of pixels of the entire document is, for example, 10%.

  On the other hand, in the image A2 obtained by reducing the image A1, the edge detection accuracy is slightly lowered due to the reduction of the image, and the number of detected edge pixels is slightly reduced, but the number of edge pixels relative to the total number of pixels of the entire document is reduced. The ratio Cs is not much different from the image A1 before reduction, and is 8%, for example. This is because characters with normal font size (not small characters) printed on a normal document are not degraded much by the edge detection even if the image is reduced. This is because it can be performed sufficiently. Accordingly, the ratio (Cs / Co) of the ratio Cs after the reduction to the ratio Co before the reduction of the document on which characters having a normal font size are printed is about “0.8”.

  On the other hand, FIG. 8A shows, for example, a document image B1 in which a line pattern is printed as a background on an A4 size paper, and characters “ABCDEFGH” (not small characters) are printed in one row thereon. FIG. 8B shows an original image B2 obtained by reducing the original (image) of FIG. 8A to a size of about 20%.

  Edges are detected for each of the images B1 and B2. In the case of the image B1, although there are few characters in the entire document, there are many line patterns in the background, and the line patterns are detected as edges, so the number of edge pixels (characters) detected is large. The ratio Co of edge pixels to the total number of pixels of the entire document is, for example, 10%.

  On the other hand, in the image B2 obtained by reducing the image B1, since the image is reduced, the interval between the parallel lines of the line pattern is further narrowed and the parallel lines are recognized as one line, and the background line pattern Are no longer detected as edges. For this reason, the ratio Cs of edge pixels to the total number of pixels of the entire document is smaller than that of the image B1 before reduction, for example, 2%. This is because the patterns of the line patterns are integrated with each other and the pattern is not recognized.

  Therefore, the ratio (Cs / Co) of the ratio Cs after the reduction to the ratio Co before the reduction of the original on which the line pattern is printed is about “0.2”. That is, the ratio (Cs / Co) before and after reduction of the image of the document is obtained, and threshold processing (for example, threshold THtc (= 0.5) or less) is performed to determine whether or not the document has a printed line pattern. Can be determined.

  Even for originals with many small characters printed, and originals with complicated halftone dots, small characters and complicated halftone dots can be reduced by reducing the size of the original so that the characters are integrated into a shape. As a result, the ratio of the original image before and after reduction (Cs / Co) is obtained and threshold processing (for example, threshold THtc (= 0. By performing the following 5), it is possible to determine whether or not the document is printed with many small characters and whether or not the document is printed with characters on complicated halftone dots.

  The reduction of the document (image) here is an image optically reduced in the sub-scanning direction (direction parallel to the scanning direction of the scanner), and the main scanning direction (direction perpendicular to the scanning direction of the scanner). ) Means an image (thinned image data) obtained by interpolation calculation (nearest naver, bilinear, bicubic, etc.).

  In addition to the method described above, an image (image data) obtained by preliminary scanning (scanning at a speed twice or four times that of main scanning) can be used as a method for reducing an original (image). it can. This is particularly effective for small characters, characters on complex halftone dots, and horizontal line manuscripts. Also, image data obtained by thinning out pixels by interpolation calculation for image data in the pre-scan and main scanning direction, image data thinned out by interpolation calculation in both the main scanning direction and the sub-scanning direction (image data subjected to low resolution conversion) Further, a Gaussian filter as shown in FIG. 9A and image data smoothed using a filter coefficient as shown in FIG. 9B can be used.

  That is, image reduction includes optical reduction during scanning and image data thinning out by interpolation calculation (decreasing resolution). A smoothing process is an effect similar to the reduction. The filter coefficient may be set to an appropriate value based on the result of performing edge detection by performing smoothing processing using various image data.

  Next, the operation of the image forming apparatus 100 will be described. 10 to 12 are flowcharts showing the procedure of the document type discrimination process. The document type discrimination process is not only configured by a dedicated hardware circuit, but is also performed by loading a computer program that defines the procedure of the document type discrimination process into the RAM and executing it by a CPU (both not shown). You can also. In the following description, the color image processing apparatus 2 is referred to as a “processing unit”.

  The processing unit performs preliminary scanning (S11), and acquires a partial image (corresponding to a pixel block) of the entire document. The processing unit converts the RGB signal obtained by the preliminary scanning into a luminance signal (L signal) (S12), performs an interpolation operation on the image data in the main scanning direction, and generates a reduced image (S13). The generated reduced image corresponds to one pixel block. The generated reduced image is stored in an image memory (not shown) for subsequent processing.

  The processing unit performs a reduced image (pixel block) region determination process (S14). Details of the area determination process will be described later. The processing unit performs comprehensive determination processing that is final region determination of the pixel block based on the result of the region determination processing (S15), and depending on the comprehensive determination result of the pixel block, the character pixel block, the halftone pixel block, or The number of pixel blocks of any other pixel block is added (S16).

  The processing unit reads the reduced image (pixel block) stored in the image memory, detects edges of the reduced image (S17), and calculates the number of edges (S18). Note that the processes in steps S14 to S16 and the processes in steps S17 and S18 can be performed in parallel. The processing unit determines whether or not the processing of all the pixel blocks has been completed (S19). If all the pixel blocks have not been processed (NO in S19), the processing unit continues the processing after step S11, acquires a partial image of the original image, and repeats the above-described processing.

  When the processing of all the pixel blocks is completed (YES in S19), the processing unit is based on the ratio of the number of pixel blocks of character pixel blocks, halftone pixel blocks, or other pixel blocks to the total number of pixel blocks of the entire document. Then, the document type is determined (S20).

  The processing unit calculates a ratio Cs of the number of edges to the number of pixels of the entire reduced document (S21), and stores the calculated ratio Cs in the memory 2212 (S22). Note that the process of step S20 and the processes of steps S21 and S22 can be performed in parallel. The processing unit performs main scanning (S23), and acquires an image of the entire document. The processing unit converts the RGB signal obtained by the main scanning into a luminance signal (L signal) (S24), and detects an edge of the converted image (an unreduced image) (S25). The processing unit calculates the number of detected edges (S26), and calculates the ratio Co of the number of edges to the number of pixels of the entire document (S27).

  The processing unit calculates the total density busyness based on the image (the image that has not been reduced) converted in step S24 (S28). Specifically, the processing unit calculates the total density busyness in four directions for each pixel of the pixel block. In addition, the process of step S25-S27 and the process of step S28 can be performed in parallel.

  The processing unit reads the ratio Cs stored in the memory 2212 in step S22, calculates the ratio (Cs / Co) of the ratio Cs to the ratio Co calculated in step S27, and the ratio (Cs / Co) is equal to or less than the threshold value THtc. It is determined whether or not (S29). When the ratio (Cs / Co) is equal to or less than the threshold value THtc (YES in S29), the processing unit determines whether or not the total density busyness calculated in step S28 has angle dependency (S30).

  When it is determined that the processing unit has angle dependency (YES in S30), the processing unit determines that the document includes a line area (area including a line pattern) (S31) and does not have angle dependency. Is determined (NO in S30), it is determined that many small characters are included in the document (S32), a document type determination signal is output (S33), and the process ends.

  On the other hand, if the ratio (Cs / Co) is not equal to or less than the threshold value THtc (NO in S29), the processing unit determines that the pseudo character area (pseudo character) is not included in the document (S34), and is determined in step S20. A document type determination signal indicating the document type determination result is output (S33).

  FIG. 13 is a flowchart showing the procedure of the area determination process. The processing unit calculates a minimum density value in the pixel block (S101), calculates a maximum density value (S102), and calculates a maximum density difference that is a difference between the calculated maximum density value and the minimum density value (S103). ). The processing unit performs a maximum density difference threshold process (S104). When the maximum density difference is larger than the threshold value THsub, the pixel block is determined as a character area or a halftone dot area (determination signal “0”), and the maximum density difference is a threshold value. If it is equal to or lower than THsub, the pixel block is determined as another region (determination signal “1”).

  The processing unit binarizes the pixel block (S105), scans the binarized data, and calculates the inversion times Kh and Kv in the main scanning direction and the sub-scanning direction (S106). The processing unit performs a reversal count threshold process (S107), and if reversal count Kh ≧ predetermined threshold THdt and reversal count Kv ≧ predetermined threshold THdt, the pixel block is a halftone area (determination signal “1”). If the number of inversions Kh <predetermined threshold THdt or the number of inversions Kv <predetermined threshold THdt, it is determined that the pixel block is a character area (determination signal “0”), and the process ends. To do. In addition, the process of step S101-S104 and the process of step S105-S107 can be performed in parallel.

  If it is determined that the document contains lines, or if it is determined that the document contains many small characters, the edge detection threshold is set so that character detection is not performed in the region separation process. Can be changed. Further, in the filter process, for a document including a line, a filter coefficient that is smoothed is smaller than that performed by a filter used for a character / print photo document in which characters and print photos are mixed, and is small. For a document containing many characters, a filter coefficient that makes the characters slightly sharper than when processing with a filter used for a character / printed photo document is used.

  Further, in the halftone processing (tone reproduction processing unit 29), dither processing or error diffusion processing that emphasizes gradation is performed on a document including a line, and a document including many small characters is processed. , Processing that emphasizes resolving power is performed. With respect to the filter coefficient, the dither matrix size and threshold value, and the diffusion coefficient for error diffusion processing, it is possible to set conditions for outputting images of various originals and obtaining high-quality images.

(Embodiment 2)
In the first embodiment, by performing preliminary scanning (pre-scanning) of a document, partial images of a predetermined size are sequentially read out of the entire image of the document, and interpolation calculation is performed on image data in the main scanning direction. Although the configuration is such that a reduced image is generated for each block, it is also possible to determine the document type without performing preliminary scanning.

  FIG. 14 is a block diagram illustrating the configuration of the document type determination unit 22 according to the second embodiment. The difference from the first embodiment (see FIG. 2) is that the luminance signal (L signal) converted by the signal conversion unit 2201 without using the interpolation calculation unit 2202 is directly used as the minimum density value calculation unit 2203 and the maximum density value. The luminance signal output from the signal conversion unit 2201 via the interpolation calculation unit 2202 is output to the calculation unit 2204 and the inversion count calculation unit 2205, and the edge detection unit 2206 and the edge detection result calculation unit 2211 are configured in two stages in parallel. In addition to being output to one edge detection unit 2206, the luminance signal is directly output to the other edge detection unit 2206 without going through the interpolation calculation unit 2202.

  In this case, the interpolation calculation unit 2202 performs interpolation calculation by thinning out pixels from the image data (luminance signal) input from the signal conversion unit 2201 in each of the main scanning direction and the sub-scanning direction. The subsequent image data (that is, the image data of the reduced image) is output to one edge detection unit 2206. The edge detection unit 2206 and the edge detection result calculation unit 2211 perform the same processing as in the first embodiment, so that the ratio Cs of the number of edge pixels (the number of edges) to the total number of pixels of the image of the entire reduced document is obtained. The calculated ratio Cs is output to the edge count ratio determination unit 2216.

  On the other hand, image data output from the signal conversion unit 2201 directly to the other edge detection unit 2206 without passing through the interpolation calculation unit 2202 (that is, image data of an unreduced image) By performing the same processing as in the first embodiment in the result calculation unit 2211, the ratio Co of the number of edge pixels (the number of edges) to the total number of pixels of the entire unreduced document image is calculated and calculated. The ratio Co is output to the edge count ratio determination unit 2216.

  The edge count ratio determination unit 2216 obtains the ratio (Cs / Co) and compares the ratio (Cs / Co) with the threshold value THtc, so that pseudo characters (for example, the font size is added to the document) as in the first embodiment. It is determined whether or not a small character area of about 4 points, a complex halftone dot character, a line pattern, or the like is included. As a result, it is possible to determine whether or not the manuscript includes pseudo characters without performing preliminary scanning.

  In the second embodiment, the determination of the maximum density difference and the number of inversions are performed using image data that has not been reduced by the interpolation calculation to determine the region such as the character region, the dot region, and other regions (document type determination). However, it is also possible to use a configuration using image data subjected to interpolation calculation.

(Embodiment 3)
In the second embodiment, the ratios Cs and Co are calculated based on the reduced image and the unreduced image with respect to the entire image of the document without performing preliminary scanning. However, the present invention is not limited to this. Instead of performing processing on the entire image of the document, it is also possible to divide the image into pixel blocks and determine whether or not a pseudo character is included in each pixel block. In this case, the document type determination (region determination) can also be performed for each pixel block.

  FIG. 15 is a block diagram illustrating the configuration of the document type determination unit 22 according to the third embodiment. The difference from the second embodiment (see FIG. 14) is that a pseudo character pixel block counting unit 2219 is provided at the subsequent stage of the edge counting ratio determining unit 2216.

  Each processing in the edge detection units 2206 and 2206, the edge detection result calculation units 2211 and 2211, and the edge count ratio determination unit 2216 is reduced to a pixel block (for example, a 4 × 4 pixel block in the case of reduced image data). In the case of non-image data, it is performed in units of 8 × 8 pixel blocks).

  Therefore, the edge detection unit 2206 and the edge detection result calculation unit 2211 in the subsequent stage of the interpolation calculation unit 2202 perform edge pixel matching with respect to the total number of pixels in the pixel block in the image data after the interpolation calculation (that is, the image data of the reduced image). A ratio Es of the number (number of edges) is calculated, and the calculated ratio Es is output to the edge count ratio determination unit 2216. On the other hand, the edge detection unit 2206 and the edge detection result calculation unit 2211 that directly acquire the image data from the signal conversion unit 2201 without using the interpolation calculation unit 2202 in the image data (that is, the image data of the unreduced image). A ratio Eo of the number of edge pixels with respect to the total number of pixels of the pixel block is calculated, and the calculated ratio Eo is output to the edge count ratio determination unit 2216.

  The edge count ratio determination unit 2216 compares the ratio (Es / Eo) with the threshold value THtc. When the ratio (Es / Eo) ≦ threshold THtc, the pixel block includes a pseudo character (for example, a small character region having a font size of about 4 points, a complex halftone character, or a line). The determination signal “1” is output to the pseudo character pixel block counting unit 2219. In addition, when the ratio (Es / Eo)> threshold THtc, the edge count ratio determination unit 2216 determines that the pixel block does not include a pseudo character, and sends a determination signal “0” to the pseudo character pixel block count unit 2219. Output to.

  The pseudo character pixel block counting unit 2219 also receives the determination result (whether or not there is an angular dependency of the total density busyness) from the total density busyness calculating unit 2218, and the pseudo character pixel block counting unit 2219 receives the total density. The determination signal “1” output by the edge count ratio determination unit 2216 is divided into whether each pixel block is a line area or an area containing many small characters based on the presence or absence of the angle dependency of the degree of complexity. Is counted. The pseudo character pixel block counting unit 2219 calculates the ratio of the count value of the determination signal calculated separately for each region to the total number of pixel blocks in the entire document, and outputs the calculated ratio to the document determination unit 2217.

  The document determination unit 2217 includes a character pixel block counting unit 2213, a halftone pixel block counting unit 2214, a ratio of each pixel block input from the other pixel block counting unit 2215, and a pseudo character pixel block counting unit 2219, and respective threshold values. Are compared to determine the type of the entire document. Note that the threshold value can be set to an appropriate value by setting the size of a pixel block to be processed and evaluating various image samples using the set pixel block.

  16 and 17 are flowcharts showing the procedure of document type discrimination processing according to the third embodiment. The processing unit converts the RGB signal obtained by the main scanning into a luminance signal (S41), and temporarily stores the converted image data in an image memory (not shown). The processing unit extracts a pixel block of a predetermined size (for example, 8 × 8 pixels) from the converted image data and performs pixel block region determination processing (S42). The area determination process is the same as the process of FIG.

  The processing unit performs comprehensive determination processing that is final region determination of the pixel block based on the result of the region determination processing (S43), and depending on the comprehensive determination result of the pixel block, the character pixel block, the halftone pixel block, or The number of pixel blocks of any other pixel block is added (S44).

  The processing unit sets the number of pixel blocks S1 including the line region and the number S2 of pixel blocks including the small character to “0” (S45), and the pixel block of a predetermined size (for example, from the image data stored in the image memory) , 8 × 8 pixels) are extracted to detect the edge of the image (S46), and the ratio Eo of the number of edges to the total number of pixels in the pixel block is calculated (S47).

  The processing unit performs interpolation calculation in the main scanning direction and the sub-scanning direction on the image data stored in the image memory, and generates a pixel block of a predetermined size (for example, 4 × 4 pixels), that is, a reduced image ( S48). The processing unit detects the edge of the reduced image (S49), and calculates the ratio Es of the number of edges to the total number of pixels in the pixel block (S50).

  The processing unit calculates the total density busyness of the pixel blocks in the image converted in step S41 (the image that has not been reduced) (S51). In addition, the process of step S46 and S47, the process of step S48-S50, and the process of step S51 can be performed in parallel.

  The processing unit calculates a ratio (Es / Eo) of the ratio Es calculated in step S50 to the ratio Eo calculated in step S47, and determines whether the ratio (Es / Eo) is equal to or less than a threshold value THtc. (S52). When the ratio (Es / Eo) is equal to or less than the threshold value THtc (YES in S52), the processing unit determines whether the total density busyness calculated in step S51 has angle dependency (S53).

  When it is determined that the processing unit has angle dependency (YES in S53), “1” is added to the number S1 of pixel blocks including the line region (S54), and when it is determined that there is no angle dependency (S54). “NO” in S53), “1” is added to the number S2 of pixel blocks including small characters (S55), and it is determined whether or not the processing of all the pixel blocks is completed (S56). When the ratio (Es / Eo) is not equal to or less than the threshold value THtc (NO in S52), the processing unit performs the processing after step S56. In addition, the process of step S42-S44 and the process of step S45-S55 can be performed in parallel.

  If all the pixel blocks have not been processed (NO in S56), the processing unit continues the processing from step S41 and repeats the above-described processing for each pixel block. When the processing of all the pixel blocks is completed (YES in S56), the processing unit performs a character pixel block, a halftone pixel block, other pixel blocks, or a pseudo character pixel block (10,000 pixels) with respect to the total number of pixel blocks of the entire document. Based on the ratio of the number of pixel blocks of a pixel block including a line pattern or a pixel block including a small character), the document type is determined (S57), a document type determination signal is output (S58), and the process ends.

(Embodiment 4)
The first to third embodiments described above are examples in which the present invention is applied to the document type determination processing. However, the scope of the present invention is not limited to this, and the present invention is applied to image region separation processing. You can also.

  FIG. 18 is a block diagram illustrating a configuration of the image forming apparatus 100 according to the fourth embodiment. The difference from the first to third embodiments is that the original type discriminating unit 22 is excluded and the region separation processing unit 24 determines whether or not there is a pseudo character region (a line pattern or a region containing many small characters). This is the point of performing region separation processing including That is, the shading correction unit 21 performs a correction process for removing various distortions generated in the illumination system, the imaging system, the imaging system, and the like of the color image input apparatus 1 on the input RGB signal, and at the same time, the color balance. The adjustment is performed, and the adjusted RGB signal is output to the input tone correction unit 23.

  FIG. 19 is a block diagram illustrating a configuration of the region separation processing unit 24 according to the fourth embodiment. The region separation processing unit 24 includes a signal conversion unit 2401, an interpolation calculation unit 2402, a minimum density value calculation unit 2403, a maximum density value calculation unit 2404, an inversion number calculation unit 2405, edge detection units 2406 and 2406, and a maximum density difference calculation unit 2407. A maximum density difference determination unit 2408, an inversion number determination unit 2409, an overall determination unit 2410, edge detection result calculation units 2411 and 2411, an edge count ratio determination unit 2416, a total density busyness calculation unit 2418, and the like.

  The area separation processing unit 24 divides image data obtained by scanning a document (which may be main scanning or preliminary scanning, but will be described below in the case of performing main scanning) into pixel blocks of a predetermined size. For each divided pixel block, character area, halftone dot area, other area, or pseudo-character area (for example, small characters with a font size of about 4 points, complex halftone characters, or lines) Region separation processing is performed.

  Whether or not the pixel block includes pseudo characters is determined by the ratio of the number of edges to the total number of pixels of the reduced image and the total number of pixels of the unreduced image, as in the first to third embodiments. And the ratio of the number of edges to. Further, the determination as to whether or not the line pattern of the pseudo characters is included in the pixel block is the sum of the total number of pixel blocks of a predetermined size of the unreduced image as in the first to third embodiments. This is performed based on the ratio of pixel blocks whose density complexity is angle-dependent. Note that the pixel block of the reduced image is generated by interpolating image data in the main scanning direction and the sub-scanning direction.

  The signal converter 2401 converts the RGB signal (RGB reflectance signal) input from the input tone correction unit 23 into a luminance signal (L signal), and temporarily converts the converted image data into an image memory (not shown). Remember. The minimum density value calculating unit 2403, the maximum density value calculating unit 2404, the inversion number calculating unit 2405, the interpolation calculating unit 2402, the edge detecting unit 2406, and the total density busyness calculating unit 2418 are pixel blocks of a predetermined size from the converted image data. (For example, 8 × 8 pixels) is extracted and each process is performed for each pixel block.

  The minimum density value calculation unit 2403 calculates the minimum density value in the pixel block, and outputs the calculated minimum density value to the maximum density difference calculation unit 2407. The maximum density value calculation unit 2404 calculates the maximum density value in the pixel block, and outputs the calculated maximum density value to the maximum density difference calculation unit 2407.

  The maximum density difference calculation unit 2407 calculates a difference (maximum density difference) between the minimum density value and the maximum density value input from the minimum density value calculation unit 2403 and the maximum density value calculation unit 2404, and the calculated maximum density difference is maximized. The data is output to the density difference determination unit 2408.

  The maximum density difference determination unit 2408 performs threshold processing on the maximum density difference input from the maximum density difference calculation unit 2407. If the maximum density difference is larger than the threshold value THsub, the pixel block is converted into a character area or a dot area (determination signal “0”). If the maximum density difference is equal to or less than the threshold value THsub, the pixel block is determined as another region (determination signal “1”), and the determination signal is output to the comprehensive determination unit 2410.

  The inversion number calculation unit 2405 obtains an average value of the image data of the pixel block, and binarizes each pixel using the average value. The inversion number calculation unit 2405 scans the binarized data in the pixel block, calculates the inversion number Kh in the main scanning direction and the inversion number Kv in the sub scanning direction, and calculates the inversion number Kh in the main scanning direction, The inversion number Kv in the scanning direction is output to the inversion number determination unit 2409.

  The inversion number determination unit 2409 determines that the pixel block is a halftone dot region (determination signal “1”) when the inversion number Kh ≧ the predetermined threshold value THdt and the inversion number Kv ≧ the predetermined threshold value THdt, and the inversion number If Kh <predetermined threshold THdt or the number of inversions Kv <predetermined threshold THdt, it is determined that the pixel block is a character region (determination signal “0”), and a determination signal is output to the comprehensive determination unit 2410.

  The interpolation calculation unit 2402 performs an interpolation calculation by thinning out pixels from the image data in the main scanning direction and the sub-scanning direction of the pixel block, and performs a pixel block of a predetermined size (for example, 4 × 4 pixels, that is, a reduction). Image data of the generated image) is generated, and the generated pixel block is output to the edge detection unit 2406 at the subsequent stage.

  The edge detection unit 2406 scans each pixel in the pixel block after the interpolation calculation as a target pixel, detects an edge by comparing the density difference between the target pixel and a neighboring pixel adjacent to the target pixel with a threshold value THed, The edge image detected for each pixel block is output to the edge detection result calculation unit 2411.

  The edge detection result calculation unit 2411 calculates the ratio Es of the number of edge pixels (the number of edges) to the total number of pixels of the reduced pixel block based on the edge image output from the edge detection unit 2406 and calculates The ratio Es is output to the edge count ratio determination unit 2416.

  The other edge detection unit 2406 scans each pixel in the pixel block that has not been reduced as a target pixel, and detects an edge by comparing the density difference between the target pixel and a neighboring pixel adjacent to the target pixel with a threshold value THed. Then, the edge image detected for each pixel block is output to the edge detection result calculation unit 2411.

  Based on the edge image input from the edge detection unit 2406, the edge detection result calculation unit 2411 calculates a ratio Eo of the number of edge pixels (number of edges) to the total number of pixels of the pixel block, and calculates the calculated ratio Eo. The data is output to the edge count ratio determination unit 2416.

  The edge count ratio determination unit 2416 obtains a ratio (Es / Eo) of the ratio Es to the ratio Eo based on the ratios Eo and Es input from the edge detection result calculation units 2411 and 2411, and the ratio (Es / Eo) and The threshold value THtc is compared. When the ratio (Es / Eo) ≦ threshold THtc, the pixel block includes a pseudo character (for example, a small character having a font size of about 4 points, a complex halftone character, a line pattern, or the like). (Determination signal “1”). In addition, when the ratio (Es / Eo)> threshold value THtc, the edge count ratio determination unit 2416 determines that the pixel block does not include a pseudo character (determination signal “0”), and combines each determination signal. The data is output to the determination unit 2410.

  The total density busyness calculation unit 2418 calculates the total density busyness between adjacent pixels in the four directions for each pixel block, and calculates the calculated four total density busyness and a predetermined threshold value. Compare. When only the total density busyness in a specific direction is smaller than the threshold value, the total density busyness calculating unit 2418 determines that the total density busyness of this pixel block has an angle dependency (determination signal “1”). In other cases, it is determined that the total density busyness of this pixel block does not have angle dependency (determination signal “0”), and each determination signal is output to the comprehensive determination unit 2410.

  The overall determination unit 2410 determines which region the pixel block is based on the determination signals input from the maximum density difference determination unit 2408, the inversion number determination unit 2409, the edge count ratio determination unit 2416, and the total density busyness calculation unit 2418. Specifically, it is finally determined whether it is a character area, halftone dot area, other area, or pseudo-character area (area including a line pattern or a small character), and an area identification signal as a determination result is output. To do.

  FIG. 20 is an explanatory diagram illustrating a final determination method of the comprehensive determination unit 2410 according to the fourth embodiment. As shown in the figure, when the determination signal from the maximum density difference determination unit 2408 is “1”, the determination signal from the inversion number determination unit 2409, the edge count ratio determination unit 2416, and the total density busyness calculation unit 2418 is related. First, the final judgment is other. Further, when the determination signal from the maximum density difference determination unit 2408 is “0” and the determination signal from the inversion number determination unit 2409 is “1”, the edge count ratio determination unit 2416 and the total density busyness calculation unit 2418. Regardless of the determination signal from, the final determination is a halftone dot.

  When the determination signals from the maximum density difference determination unit 2408, the inversion number determination unit 2409, and the edge count ratio determination unit 2416 are “0”, the final determination is performed regardless of the determination signal from the total density busyness calculation unit 2418. Becomes a character. Furthermore, when the determination signal from the maximum density difference determination unit 2408 and the inversion number determination unit 2409 is “0” and the determination signal from the edge count ratio determination unit 2416 is “1”, the total density busyness calculation unit 2418. When the determination signal from “0” is “0”, the final determination is an area including many small characters. When the determination signal from the total density busyness calculation unit 2418 is “1”, the final determination is a line pattern. It becomes the area including.

  21 and 22 are flowcharts showing the procedure of the region separation process according to the fourth embodiment. The processing unit converts the RGB signal obtained by the main scanning into a luminance signal (S71), and temporarily stores the converted image data in the image memory. The processing unit extracts a pixel block of a predetermined size (for example, 8 × 8 pixels) from the converted image data, performs pixel block region determination processing (S72), and determines a determination signal (specifically, the maximum density difference). The determination signal output from the determination unit 2408 and the inversion number determination unit 2409 is stored (S73). The area determination process is the same as the process of FIG.

  The processing unit extracts a pixel block of a predetermined size (for example, 8 × 8 pixels) from the image data stored in the image memory, detects an edge of the image (S74), and determines the number of edges relative to the total number of pixels of the pixel block. The ratio Eo is calculated (S75).

  The processing unit performs interpolation calculation in the main scanning direction and the sub-scanning direction on the image data stored in the image memory, and generates a pixel block of a predetermined size (for example, 4 × 4 pixels), that is, a reduced image ( S76). The processing unit detects the edge of the reduced image (S77), and calculates the ratio Es of the number of edges to the total number of pixels in the pixel block (S78). The processing unit calculates the total density busyness of the image (the image that has not been reduced) converted in step S71 (S79). Note that the processes of steps S74 and S75, the processes of steps S76 to S78, and the process of step S79 can be performed in parallel.

  The processing unit determines whether the determination signal output from the edge count ratio determination unit 2416 based on whether the ratio (Es / Eo) is equal to or less than the threshold value THtc, and whether the total density busyness has an angle dependency. The determination signal output from the total density busyness calculation unit 2418 is stored based on the determination (S80). In addition, the process of step S72 and S73 and the process of step S74-S80 can be performed in parallel.

  The processing unit performs comprehensive determination processing for pixel block region separation based on the determination signals stored in steps S73 and S80 (S81). Specifically, when the determination signal output from the maximum density difference determination unit 2408 is “1”, the processing unit separates the pixel block to be processed into other regions and outputs from the maximum density difference determination unit 2408. When the determined determination signal is “0” and the determination signal output from the inversion number determination unit 2409 is “1”, the pixel block to be processed is separated into halftone areas.

  In addition, when the determination signals output from the maximum density difference determination unit 2408, the inversion number determination unit 2409, and the edge count ratio determination unit 2416 are “0”, the processing unit separates the pixel block to be processed into character regions. . Further, when the determination signal output from the maximum density difference determination unit 2408 and the inversion number determination unit 2409 is “0” and the determination signal output from the edge count ratio determination unit 2416 is “1”, the processing unit is “1”. In addition, when the determination signal output from the total density busyness calculation unit 2418 is “0”, the pixel block to be processed is separated into regions containing many small characters, and is output from the total density busyness calculation unit 2418. When the determination signal is “1”, the pixel block to be processed is separated into regions including line patterns.

  The processing unit determines whether or not the processing of all the pixel blocks has been completed (S82). If the processing of all the pixel blocks has not been completed (NO in S82), the processing unit performs the processing from step S71 onward. And the above-described processing for each pixel block is repeated. When the processing for all the pixel blocks is completed (YES in S82), the processing unit ends the processing.

  When it is determined that the pixel block is an area including a line, a filter coefficient that is smoothed in the filter process, rather than performing a process using a filter used for a character / printed photo document in which characters and a printed photo are mixed In the halftone processing, dither processing or error diffusion processing that emphasizes gradation is performed. Also, if it is determined that the pixel block is an area including many small characters, the filter processing uses a filter coefficient that makes the characters slightly sharper than the processing performed by the filter used for characters / printed photo originals. In the processing, processing with an emphasis on resolution is performed.

  As described above, in the present invention, when the image includes small characters, characters on complex halftone dots, or pseudo characters such as various lines of lines, only the presence or absence of the pseudo characters In addition, it is possible to accurately determine whether a small number of pseudo characters are included or whether a line of pseudo characters is included, and image quality can be improved by performing processing corresponding to each. Deterioration can be prevented. Also, the image can be divided into required pixel blocks, and the presence or absence of pseudo characters can be determined for each pixel block. Further, it can be accurately determined whether or not the document is a document including pseudo characters.

  In addition, when region separation processing is performed on an area including a large number of small characters, such as a font size of 4 points, or a complex halftone dot character (an area printed with small characters on a colored area), the character area In some cases, it is determined that the area is a halftone dot area, and in some cases, the area is not correctly determined as a character area. As a result, the region that is determined to be a character region when performing the filtering process is emphasized with an edge, and the region that is determined to be a halftone dot region (for example, a complicated part of “hen” or “making”) Since the image is smoothed, the image is partially blurred and the image quality is deteriorated. Further, the area including the line should be subjected to smoothing processing, but is easily detected as an edge, so that enhancement processing is performed, and in this case, the image quality also deteriorates. Therefore, it is determined whether or not many small characters are included or whether or not a line is included. If many small characters or a line is included, the region separation process The image quality degradation can be suppressed by changing the threshold value when detecting the edge so as to make it difficult to detect the region.

  In the above-described embodiment, edges are detected, the ratio between the number of detected edges and the total number of pixels of the document or pixel block is calculated, and the ratio in the reduced or low resolution converted image and the reduced or low resolution conversion are calculated. In this configuration, the presence or absence of pseudo characters is determined by comparing the ratio (ratio) with the ratio in the image that has not been processed to a predetermined threshold. The configuration may be such that the ratio of the number of edges in an image that has not undergone resolution conversion is compared with a threshold value. In this case, if the threshold value is changed according to the reduction rate or the low resolution conversion rate, the same effect can be obtained, and the presence or absence of a pseudo character can be determined.

  In the above-described embodiment, as the color image input device 1, for example, a flat bed scanner, a film scanner, a digital camera, a mobile phone, or the like is used. Further, as the color image output device 3, for example, an image display device such as a CRT display or a liquid crystal display, an electrophotographic system or an ink jet system printer that outputs processing results to recording paper or the like is used. Further, the image forming apparatus 100 may include a modem as a communication unit for connecting to a server apparatus or the like via a network. Further, instead of acquiring the color image data from the color image input device 1, the color image data may be acquired from an external storage device, a server device, or the like via a network.

  The present invention performs a document type determination process, an area separation process, and the like on a computer-readable recording medium in which a program code (execution format program, intermediate code program, source program) of a computer program to be executed by a computer is recorded. Program code can also be recorded. As a result, it is possible to provide a portable recording medium on which the program code for performing the above processing is recorded. The recording medium may be a non-illustrated memory, for example, a program medium such as a ROM because processing is performed by a microcomputer, and a program reading device as an external storage device (not illustrated) is provided, and the recording medium is stored therein. It may be a program medium that can be read by being inserted.

  In any case, the stored program code may be configured to be accessed and executed by the microprocessor, or the program code is read, and the read program code is a program (not shown) of the microcomputer. The program code may be downloaded to the storage area and executed. In this case, it is assumed that the computer program for download is stored in the main device in advance.

  Here, the program medium is a recording medium configured to be separable from the main body, such as a tape system such as a magnetic tape or a cassette tape, a magnetic disk such as a flexible disk or a hard disk, and a CD-ROM / MO / MD / DVD. Semiconductors such as optical discs, IC cards (including memory cards) / optical cards, etc., or mask ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash ROM, etc. It may be a medium that carries a fixed program code including a memory.

  In this case, since the system configuration is capable of connecting a communication network including the Internet, the medium may be a medium that dynamically carries the program code so as to download the program code from the communication network. When the program code is downloaded from the communication network in this way, the computer program for downloading may be stored in the main device in advance or may be installed from another recording medium. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission.

1 is a block diagram illustrating a configuration of an image forming apparatus including an image processing apparatus according to the present invention. 4 is a block diagram illustrating a configuration of a document type determination unit. FIG. It is explanatory drawing which shows the final determination method of a comprehensive determination part. It is explanatory drawing which shows an example of the edge detection method. It is a schematic diagram for demonstrating the determination method of a line area | region. FIG. 6 is an explanatory diagram illustrating a document determination method of a document determination unit. It is a schematic diagram which shows an example of the image of the original in which the character was printed. It is a schematic diagram showing an example of an image of a document in which characters are printed on a line pattern. It is a schematic diagram for demonstrating the filter used for the smoothing process of image data. 6 is a flowchart illustrating a procedure of document type determination processing. 6 is a flowchart illustrating a procedure of document type determination processing. 6 is a flowchart illustrating a procedure of document type determination processing. It is a flowchart which shows the procedure of an area | region determination process. 6 is a block diagram illustrating a configuration of a document type determination unit according to Embodiment 2. FIG. FIG. 10 is a block diagram illustrating a configuration of a document type determination unit according to a third embodiment. 10 is a flowchart illustrating a procedure of document type determination processing according to the third embodiment. 10 is a flowchart illustrating a procedure of document type determination processing according to the third embodiment. FIG. 10 is a block diagram illustrating a configuration of an image forming apparatus according to a fourth embodiment. FIG. 10 is a block diagram illustrating a configuration of a region separation processing unit according to a fourth embodiment. FIG. 10 is an explanatory diagram illustrating a final determination method of a comprehensive determination unit according to a fourth embodiment. 14 is a flowchart illustrating a procedure of region separation processing according to the fourth embodiment. 14 is a flowchart illustrating a procedure of region separation processing according to the fourth embodiment.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Color image input device 2 Color image processing device 3 Color image output device 22 Original type discrimination | determination part 24 Area | region separation processing part 2201,2402 Signal conversion part 2202,2402 Interpolation operation part 2203,2403 Minimum density value calculation part 2204,2404 Maximum density Value calculation unit 2205, 2405 Inversion number calculation unit 2206, 2406 Edge detection unit 2207, 2407 Maximum density difference calculation unit 2208, 2408 Maximum density difference determination unit 2209, 2409 Inversion number determination unit 2210, 2410 General determination unit 2211, 2411 Edge detection Result calculation unit 2212 Memory 2213 Character pixel block counting unit 2214 Halftone pixel block counting unit 2215 Other pixel block counting units 2216 and 2416 Edge count ratio determination unit 2217 Document determination units 2218 and 2418 Sum total Busyness calculating section 100 an image forming apparatus

Claims (22)

In an image processing method for performing a process of determining the type of the image based on a pixel value of the pixel of an image of a document composed of a plurality of pixels,
A first edge detecting step of detecting an edge pixel based on a pixel value of each pixel of the image;
A first calculation step of calculating a first feature amount that is a ratio of the number of edge pixels to the total number of pixels of the image of the entire document based on the edge pixels detected in the first edge detection step;
A reduction step of reducing the image;
A second edge detection step for detecting an edge pixel based on a pixel value of each pixel of the image reduced in the reduction step;
A second calculation step of calculating a second feature amount, which is a ratio of the number of edge pixels to the total number of pixels of the image of the entire reduced document, based on the edge pixels detected in the second edge detection step;
A ratio calculating step for calculating a ratio of the second feature quantity calculated in the second calculation step to the first feature quantity calculated in the first calculation step;
A feature amount calculating step for calculating a feature amount related to the density distribution of the image for a plurality of directions;
An angle dependency determining step for determining the presence or absence of angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step;
An image processing method comprising: a determination step of determining a type of the image based on the ratio calculated in the ratio calculation step and the determination result in the angle dependency determination step. The first calculation step includes
A first counting step for counting the number of edge pixels detected in the first edge detecting step;
Calculating a first ratio of the number of edge pixels counted in the first counting step to the total number of pixels in the image,
The second calculating step includes
A second counting step of counting the number of edge pixels detected in the second edge detecting step;
The method according to claim 1, further comprising: calculating a second ratio of the number of edge pixels counted in the second counting step to the total number of pixels in the image reduced in the reduction step. Image processing method. The determination step includes
A determination step of determining whether the ratio calculated in the ratio calculation step is equal to or less than a predetermined threshold;
Determining that the ratio is equal to or less than a predetermined threshold in the determination step, and determining that the image includes a line when there is a determination result in the angle dependency determination step. The image processing method according to claim 1 or 2. In an image processing method for performing a process of determining the type of the image based on a pixel value of the pixel of an image of a document composed of a plurality of pixels,
A first edge detecting step of detecting an edge pixel based on a pixel value of each pixel of the image;
A third calculation step of calculating a third feature amount that is the number of edge pixels of the document;
A reduction step of reducing the image;
A second edge detection step for detecting an edge pixel based on a pixel value of each pixel of the image reduced in the reduction step;
A fourth calculation step of calculating a fourth feature amount that is the number of edge pixels of the reduced document;
A second ratio calculating step for calculating a ratio of the fourth feature amount calculated in the fourth calculation step to the third feature amount calculated in the third calculation step;
A feature amount calculating step for calculating a feature amount related to the density distribution of the image for a plurality of directions;
An angle dependency determining step for determining the presence or absence of angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step;
A second determination step of determining the type of the image based on the ratio calculated in the second ratio calculation step and the determination result in the angle dependence determination step;
An image processing method comprising: The second determination step includes
A determination step of determining whether the ratio calculated in the second ratio calculation step is equal to or less than a predetermined threshold;
Determining that the ratio is equal to or less than a predetermined threshold in the determination step, and determining that the image includes a line when there is a determination result in the angle dependency determination step;
The image processing method according to claim 4, further comprising: In an image processing apparatus for performing a process of determining the type of the image based on a pixel value of the pixel of an image of a document including a plurality of pixels,
First edge detection means for detecting an edge pixel based on a pixel value of each pixel of the image;
First calculation means for calculating a first feature amount, which is a ratio of the number of edge pixels to the total number of pixels of the image of the entire document , based on the edge pixels detected by the first edge detection means;
Reduction means for reducing the image;
Second edge detection means for detecting an edge pixel based on the pixel value of each pixel of the image reduced by the reduction means;
Second calculation means for calculating a second feature amount, which is a ratio of the number of edge pixels to the total number of pixels of the image of the entire reduced document, based on the edge pixels detected by the second edge detection means;
A ratio calculation means for calculating a ratio of the second feature quantity calculated by the second calculation means to the first feature quantity calculated by the first calculation means;
A feature amount calculating means for calculating a feature amount related to the density distribution of the image for a plurality of directions;
Angle dependency determination means for determining the presence or absence of angle dependency of the density distribution of the image based on the feature value in each direction calculated by the feature value calculation means;
An image processing apparatus comprising: a determination unit that determines a type of the image based on a ratio calculated by the ratio calculation unit and a determination result of the angle dependency determination unit. The first calculation means includes
First counting means for counting the number of edge pixels detected by the first edge detecting means;
Means for calculating a first ratio of the number of edge pixels counted by the first counting means to the total number of pixels in the image;
The second calculation means includes
Second counting means for counting the number of edge pixels detected by the second edge detecting means;
7. The image processing according to claim 6 , further comprising: a unit that calculates a second ratio of the number of edge pixels counted by the second counting unit to the total number of pixels in the image reduced by the reduction unit. apparatus. The feature amount calculating means is configured to calculate a sum of absolute values of differences between pixel values of adjacent pixels for each direction of the image,
The image processing apparatus according to claim 6, wherein the angle dependency determination unit is configured to determine whether or not there is angle dependency based on the sum calculated for each direction. . The determination means includes
Determination means for determining whether the ratio calculated by the ratio calculation means is equal to or less than a predetermined threshold;
And a means for determining that the image includes a line when the determination means determines that the ratio is equal to or less than a predetermined threshold value and the determination result of the angle dependence determination means is present. The image processing apparatus according to any one of claims 6 to 8 . The determination unit determines that the image includes lowercase letters or halftone dots when the determination unit determines that the ratio is equal to or less than a predetermined threshold and the determination result of the angle dependency determination unit is none. The image processing apparatus according to claim 9 , further comprising: The image processing apparatus according to claim 6 , wherein the determination unit is configured to determine a type of an image for each pixel block including a plurality of pixels. Means for counting the number of pixel blocks determined to be the same type by the determination means;
The image processing apparatus according to claim 11 , further comprising: a unit that determines a type of a document from which the image is obtained based on the number of pixel blocks counted by the unit. In an image processing apparatus for performing a process of determining the type of the image based on a pixel value of the pixel of an image of a document including a plurality of pixels,
First edge detection means for detecting an edge pixel based on a pixel value of each pixel of the image;
Third calculating means for calculating a third feature amount that is the number of edge pixels of the document;
Reduction means for reducing the image;
Second edge detection means for detecting an edge pixel based on the pixel value of each pixel of the image reduced by the reduction means;
Fourth calculating means for calculating a fourth feature amount that is the number of edge pixels of the reduced document;
Second ratio calculating means for calculating a ratio of the fourth feature value calculated by the fourth calculating means to the third feature value calculated by the third calculating means;
A feature amount calculating means for calculating a feature amount related to the density distribution of the image for a plurality of directions;
Angle dependency determination means for determining the presence or absence of angle dependency of the density distribution of the image based on the feature value in each direction calculated by the feature value calculation means;
Second determination means for determining the type of the image based on the ratio calculated by the second ratio calculation means and the determination result of the angle dependence determination means;
An image processing apparatus comprising: The feature amount calculating means is configured to calculate a sum of absolute values of differences between pixel values of adjacent pixels for each direction of the image,
  The angle dependency determining means is configured to determine the presence or absence of angle dependency based on the sum calculated for each direction.
The image processing apparatus according to claim 13. The second determination means includes
Determining means for determining whether the ratio calculated by the second ratio calculating means is equal to or less than a predetermined threshold;
Means for determining that the image includes a line when the determination means determines that the ratio is equal to or less than a predetermined threshold value and the determination result of the angle dependence determination means is;
The image processing apparatus according to claim 13 or 14, characterized by comprising: The second determination means is an image including lowercase letters or halftone characters when the determination means determines that the ratio is equal to or less than a predetermined threshold value and the determination result of the angle dependence determination means is none. The image processing apparatus according to claim 15, further comprising a determination unit. The image processing apparatus according to claim 13, wherein the second determination unit is configured to determine an image type for each pixel block including a plurality of pixels. . Means for counting the number of pixel blocks determined to be of the same type by the second determination means;
  Means for discriminating the type of document from which the image is obtained based on the number of pixel blocks counted by the means;
The image processing apparatus according to claim 17, further comprising: An image processing device according to any one of claims 6 to 18 ,
An image forming apparatus comprising: an image forming unit that forms an output image based on an image processed by the image processing apparatus. In a computer program for causing a computer to determine the type of an image based on a pixel value of the pixel of an image of a document composed of a plurality of pixels,
A first edge detection step for causing a computer to detect an edge pixel based on a pixel value of each pixel of the image;
A first calculation step of causing a computer to calculate a first feature amount that is a ratio of the number of edge pixels to the total number of pixels of an image of the entire document , based on the edge pixels detected in the first edge detection step;
A reduction step of causing the computer to reduce the image;
A second edge detection step for causing the computer to detect an edge pixel based on a pixel value of each pixel of the image reduced in the reduction step;
A second calculation step of causing the computer to calculate a second feature amount that is a ratio of the number of edge pixels to the total number of pixels of the image of the entire reduced document based on the edge pixels detected in the second edge detection step; When,
A ratio calculation step for causing a computer to calculate a ratio of the second feature quantity calculated in the second calculation step to the first feature quantity calculated in the first calculation step;
A feature amount calculating step for causing the computer to calculate a feature amount related to the density distribution of the image for a plurality of directions;
An angle dependency determining step for causing a computer to determine the presence or absence of angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step;
A computer program comprising: a determination step for causing a computer to determine the type of the image based on the ratio calculated in the ratio calculation step and the determination result in the angle dependency determination step. In a computer program for causing a computer to determine the type of an image based on a pixel value of the pixel of an image of a document composed of a plurality of pixels,
A first edge detection step for causing a computer to detect an edge pixel based on a pixel value of each pixel of the image;
A third calculation step for causing the computer to calculate a third feature amount that is the number of edge pixels of the document;
A reduction step of causing the computer to reduce the image;
A second edge detection step for causing the computer to detect an edge pixel based on a pixel value of each pixel of the image reduced in the reduction step;
A fourth calculation step of causing a computer to calculate a fourth feature amount that is the number of edge pixels of the reduced document;
A second ratio calculating step for causing the computer to calculate a ratio of the fourth feature quantity calculated in the fourth calculation step to the third feature quantity calculated in the third calculation step;
A feature amount calculating step for causing the computer to calculate a feature amount related to the density distribution of the image for a plurality of directions;
An angle dependency determining step for causing a computer to determine the presence or absence of angle dependency of the density distribution of the image based on the feature amount in each direction calculated in the feature amount calculating step;
A determination step for causing the computer to determine the type of the image based on the ratio calculated in the second ratio calculation step and the determination result in the angle dependence determination step;
A computer program comprising: A computer-readable recording medium on which the computer program according to claim 20 or 21 is recorded.

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