KR20040092561A - Method for segmenting Scan Image - Google Patents

Abstract

PURPOSE: An image area division method for a scan image is provided to divide a local area into a continuous tone image area, a character area and a binary image area. CONSTITUTION: An input scan image is divided into local areas(101). The maximum tone difference value and the minimum tone difference value of respective pixels included in the divided local areas are obtained(102). An accumulation maximum tone difference value, a direction value and a mean tone difference value of the respective local areas are obtained using the maximum tone difference value and the minimum tone difference value(103). The respective local areas are divided into a blank area, a continuous tone image area, a character area or a binary image area(104-109). The divided respective areas are post-processed to correct an error.

Description

Method for segmenting scan image {Method for segmenting Scan Image}

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for commercially separating a scanned image and a computer-readable recording medium recording a program for realizing the method. More particularly, the present invention relates to a charge coupled device (CCD) or a contact image sensor (CIS). : A method for separating a scanned image into a continuous gradation image region, a character region, and a binary image region, and a computer-readable recording medium recording a program for realizing the method. It is about.

Here, the gradation refers to the level of concentration that changes from the bright to the dark, and even the gradation refers to the difference.

In general, a method using frequency analysis is mainly used as a method of analyzing a given image and dividing it into an image area and a text area. To do this, use a Fast Fourier Transform (FFT) or calculate a system even with neighboring pixels (horizontal or vertical) in a local region of N (horizontal) * M (vertical) pixels. The method using the distribution of is mainly used.

An image having continuous gradation, such as a photograph, has a low frequency characteristic in which the gradation gradually changes due to its characteristics. Therefore, when the continuous grayscale image is divided into N * M local regions and FFT transformed, the low frequency value is larger than that of the character region. On the contrary, in the case of the character area, the gray level changes abruptly at the boundary of the character stroke, so that the value of the high frequency part appears larger than the continuous gray image area such as a photograph during FFT conversion.

However, FFTs are not suitable for analyzing images in real time because they require quite a lot of mathematical operations due to their computational characteristics. Therefore, many methods are used in place of frequency analysis using even the system, which calculates even the system with neighboring pixels in the local area, and there are many small values close to 0 in the histogram of this value. The image can be distinguished by an image having strong surface low frequency, that is, a continuous gradation image region such as a photograph. On the contrary, a large number of large values exist in the distribution of values even with neighboring pixels and a value of 0 even in the rest of the total distribution indicates If it is occupied, the high frequency is strong, so it is determined by the character area.

In this case, the reason why the value of the text area is 0 is the most part of the entire distribution, because the background of the text is usually white or non-white. This is because the inside of the stroke is a constant color, in most cases black.

With the development of printing technology, binary output images are used in many books, newspapers, magazines, etc., and the outputs of inkjet printers and laser printers spread from house to house are also binary output images. As shown in FIG. 1, since the binary output image is network-processed and outputs a continuous grayscale image through an error diffusion method or a binarization using a dither screen, it appears as a continuous grayscale image to the human eye, but is actually continuous when the FFT is converted into a binary image. Unlike the gradation image, the high frequency component appears very strong. Therefore, there is a problem in that the conventional method is not easy to separate the binary image included in the character area.

In addition, in the case of a scanned image through a CIS or CCD, even if the same original is scanned according to the scanning error of the CCD or CIS device, the values are different for each device. As shown in FIG. 2, even a system having a considerably diverse system has a distribution value, and thus there is a problem in that a conventional method is not easy to separate binary images included in a character area.

The present invention has been proposed to solve the above-mentioned problems. After dividing an input scan image into local regions, the average gray scale value, the cumulative maximum scale value and the direction value of the local region are obtained, and the values are compared with a predetermined threshold value. It is an object of the present invention to provide a method of separating a scan image for dividing the local area into a continuous gradation image area, a character area and a binary image area, and a computer-readable recording medium recording a program for realizing the method. .

1 is an exemplary view of a binary image used in the present invention.

2 is an exemplary view of an image scanned through a CIS or CCD used in the present invention.

Figure 3 is an embodiment explanatory diagram for the direction of the maximum even value and the minimum even value according to the present invention.

4 is a structural diagram of an embodiment of a local region having eight directions according to the present invention;

5 is a structural diagram of an embodiment of a local region having four directions according to the present invention;

Fig. 6 is an exemplary view of a test input image used in the method for phase separation of a scanned image according to the present invention.

Figure 7 is a result of one embodiment by the method of reverse separation of the scanned image according to the present invention.

FIG. 8 is an exemplary view illustrating a result of closing a test input image separated by a phase separation method of a scanned image according to the present invention; FIG.

FIG. 9 is an exemplary view illustrating a result of blob coloring of a result of closing a test input image separated by a phase separation method of a scanned image according to the present invention; FIG.

10 is a flowchart illustrating a method for separating a scan image from a scan image according to the present invention.

* Explanation of symbols for the main parts of the drawings

61: Scanned gradation monochrome image 62: Scanned gradation color image

63: Scanned binary image 71,72: Phase separated continuous gradation image area

73: binary separated image region

According to an aspect of the present invention, there is provided a method for performing phase separation of a scanned image, comprising: a first step of dividing an input scan image into local regions; Obtaining a maximum even value and a minimum even value of each pixel included in the divided local region; A third step of obtaining a cumulative maximum even value, a direction value, and an average even value of each local area by using the maximum even value and the minimum even value; A fourth step of separating each local area into a blank area, a continuous gray level image area, a character area, or a binary image area by using the obtained cumulative maximum even value, direction value, and average gray level value; And a fifth step of correcting an error by performing a post-processing process on each of the separated areas.

On the other hand, the present invention, in the separation of the scanning image, the apparatus for separating the scanning image with a processor, comprising: a first function of dividing the input scan image into local regions; A second function of obtaining a maximum even value and a minimum even value of each pixel included in the divided local region; A third function of obtaining a cumulative maximum even value, a direction value, and an average even value of each local area by using the maximum even value and the minimum even value; A fourth function of dividing each local area into a blank area, a continuous gray level image area, a character area, or a binary image area by using the obtained cumulative maximum even value, direction value, and average gray level value; And a computer-readable recording medium having recorded thereon a program for realizing a fifth function of correcting errors by performing post-processing on each of the separated areas.

The above objects, features and advantages will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Figure 3 is an exemplary explanatory diagram of the direction of the maximum system even value and the minimum system even value according to the present invention.

As shown in FIG. 3, the maximum system even value MAXDIFF becomes a system even in the horizontal (horizontal) direction pixel, and in most cases, the minimum system even value MINDIFF can be obtained in the direction perpendicular to the maximum system even in the value direction. Can be. The minimum system even value is the system even in the direction having the smallest system value among the various directions.

Looking at this in more detail, when the center of the vertical line as the reference pixel, it can be seen that the difference in density of each pixel in the horizontal direction remains the largest. On the other hand, it can be seen that the concentration difference remains the smallest in the vertical direction. Therefore, it can be seen that the value even of the horizontal pixel becomes the maximum value, and even the value of the vertical pixel becomes the minimum value.

The continuous gradation image area and the character area can be distinguished by the cumulative maximum system value obtained by accumulating the maximum even value. However, in the networked binary image as shown in FIG. 1, even the cumulative maximum value has a large value similar to that of the character area. Therefore, the binary image area included in the character area is distinguished using the direction value accumulated by subtracting even the minimum system value from the maximum system even value.

In more detail, since the binary image area does not have a directional stroke like a character, even the maximum value minus the minimum value has a small value, and the accumulated value also has a small value. This makes it easier to distinguish between character and binary areas.

4 is a structural diagram of an embodiment of a local region having eight directions according to the present invention.

As shown in FIG. 4, the local regions having eight directions are pixels A, B, C, D, E, F, G, H, I, J separated by two pixels horizontally or vertically from the pixels i, j. , K, M, N, O, P.

In more detail, to divide the input image, it is divided into N * M local regions. Each local area has I * J pixels according to the resolution of the input image. The size of the local region is about 8 * 8 in a 300 DPI (Dot Per Inch) image and about 16 * 16 in a 600 DPI image. The reason is that in a 300 DPI image, 8 pixels is about 0.68 mm, a unit small enough for area separation, and is similar in thickness to the stroke of a human-written character. The following Equation 1 is calculated from the pixels in the N * M region thus divided.

Here, n and m in Equation 1 indicate the n and m local regions when the entire image is divided into N vertical and M horizontal regions, and the size of the local region is I row J columns. i, j represent the vertical i-th horizontal j-th pixel in this local area. In addition, the maximum system even value (i, j) is obtained as shown in [Equation 2] below, and the minimum system even value (i, j) as shown in [Equation 3] shown below.

Even maximum value (i, j) = MAX (| A-I |, | B-J |, | C-K |, | D-L |, | E-M |, | F-N |, | G-O |, | H-P |)

Even minimum value (i, j) = MIN (| A-I |, | B-J |, | C-K |, | D-L |, | E-M |, | F-N |, | G-O |, | H-P |)

Here, when the direction value is calculated using the maximum even value and the minimum even value, Equation 4 below.

Here, if the direction value of Equation 4 has a value larger than a threshold value, the corresponding image area may be determined as a character area, and if the direction value of Equation 4 has a smaller value, it may be determined as a binary image area.

On the other hand, the average gradation value is shown in Equation 5 below. Here, P (i, j) means a gray value of the i, j pixel.

Since the area used as the background of the character image is white in most cases, it is regarded as the background area when the average gradation value of Equation 5 is larger than the threshold value.

5 is a structural diagram of an embodiment of a local region having four directions according to the present invention.

As shown in Fig. 5, the local region having four directions has pixels A, B, C, D, E, F, G, and H separated by one pixel in the horizontal or vertical direction from the pixels i, j.

Here, the maximum even value and the minimum even value are obtained as shown in Equation 6 below.

Even maximum value (i, j) = MAX (| A-E |, | B-F |, | C-G |, | D-H |)

Even minimum value (i, j) = MIN (| A-E |, | B-F |, | C-G |, | D-H |)

In the present invention, only eight directions and four directions have been described as an example, but may be implemented in two directions.

At this time, the maximum system even value and the minimum system even value are as shown in Equation 7 below with reference to FIG. 4.

Even maximum value (i, j) = MAX (| A-I |, | E-M |)

Even minimum value (i, j) = MIN (| A-I |, | E-M |)

6 is an exemplary view of a test input image used in the method for performing phase separation of a scanned image according to the present invention.

As shown in FIG. 6, the test input image used in the phase separation method of the scanned image according to the present invention is an image scanned using a 300 DPI CCD scanner, and a continuous grayscale monochrome image 61 and a lower left corner are located at the upper left corner. Includes a continuous grayscale color image 62 and a binary image 63 in the middle of the right side.

7 is a diagram illustrating an exemplary embodiment of a method for performing phase separation of scanned images according to the present invention.

As shown in FIG. 7, the continuous grayscale image areas 71 and 72 shown as black portions, the binary image area 73 shown as gray portions, and the characters shown as white portions by the method of phase separation of scanned images according to the present invention. It can be seen that the regions are all correctly determined.

However, it can be seen that a part of the character area is identified as a binary image area, because the binary image area extends over the edge or the end of the character stroke. However, since these areas do not appear continuously, they can be removed by performing an opening operation in the post-processing process.

In addition, there are portions (white) in which portions corresponding to the edge of the image are identified as the character area among the portions determined as the continuous gradation image. This is because the boundary of the image has the same orientation as the character, and thus can be removed through the image filling operation during the post-processing. The result is shown in FIG.

In addition, FIG. 9 is a result of performing a blob coloring process and image area squarer on the image of FIG. 8, and it can be seen that three images in the document are accurately separated.

Here, the image processing operation, the image filling operation, the blob coloring process, and the image area visualization are referred to as the post-processing process.

The present invention can be applied to a multifunction printer, a copier, a scanner, or the like, so that the devices can be partially scanned. That is, only one of the image or the text can be scanned in the document including the image and the text.

FIG. 10 is a flowchart illustrating a method for performing phase separation of a scanned image according to the present invention.

First, after dividing the input scanned image into local regions of N (length) * M (horizontal) size, the maximum and even minimum values of the pixels of the divided local regions are calculated (102).

Subsequently, the average system value, the cumulative maximum system value, and the direction value of each local region are calculated using the maximum system even value and the minimum system even value (103).

Thereafter, it is determined whether the average even value obtained in step 103 is greater than a predetermined threshold (104). As a result of determination, it is determined as a blank area if large. This makes it easier to separate the continuous gradation image area from the binary image area and the character area. On the other hand, if it is small, it is determined (106) whether the cumulative maximum system value obtained in the process "103" is larger than a predetermined threshold value. As a result of the determination, if it is smaller than the threshold value, it is determined as a continuous gradation image area (107). Due to the above process 106, the continuous gradation image area and the character area are separated. Now, the binary image area can be separated from the text area. To this end, it is determined 108 whether the direction obtained in step 103 is greater than a predetermined threshold. If it is larger than the threshold value, it is determined as a character area (109), and if it is small, it is determined as a binary image area (110).

Through the above processes, the scanned image is divided into a continuous gray image area, a text area, and a binary image area. Therefore, in the subsequent post-processing process, both the binary image area and the continuous gradation image area are regarded as the picture area without being separated.

As described above, the method of the present invention may be implemented as a program and stored in a recording medium (CD-ROM, RAM, ROM, floppy disk, hard disk, magneto-optical disk, etc.) in a computer-readable form. Since this process can be easily implemented by those skilled in the art will not be described in more detail.

The present invention described above is not limited to the above-described embodiments and the accompanying drawings, and various substitutions, modifications, and changes are possible in the art without departing from the technical spirit of the present invention. It will be clear to those of ordinary knowledge.

As described above, the present invention has the effect of being processed in the same way as the electronic image not only for the electronic input image but also for the scanned image applied to a multifunction apparatus, a copier, a scanner, and the like that support various resolutions.

In addition, the present invention is applied to a multifunction device, a copier, a scanner, and the like, which supports various resolutions, to not only read a specific image area among image areas, but also to apply an algorithm suitable for the characteristics of the separated area to improve image quality. There is an effect that can increase the processing speed.

In addition, the present invention has an effect that can be applied to an embedded system with low computational capability to improve the performance of the embedded system by separating the station by simple operation as soon as possible.

Claims (8)

In the phase separation method of the scanned image, Dividing the input scan image into local regions; Obtaining a maximum even value and a minimum even value of each pixel included in the divided local region; A third step of obtaining a cumulative maximum even value, a direction value, and an average even value of each local area by using the maximum even value and the minimum even value; A fourth step of separating each local area into a blank area, a continuous gray level image area, a character area, or a binary image area by using the obtained cumulative maximum even value, direction value, and average gray level value; And A fifth step of correcting an error by performing a post-processing process on each of the separated regions Phase separation method of the scan image comprising a. The method of claim 1, The fourth step, A sixth step of determining the local area as a blank area when the average gradation value obtained in the third step is larger than a first threshold value; A seventh step of determining the local area as a continuous gradation image area when the average gradation value obtained in the third step is not larger than the first threshold value and even the cumulative maximum value is not larger than the second threshold value; When the average gradation value obtained in the third step is not greater than the first threshold value, even the cumulative maximum sum value is larger than the second threshold value and the directionality is larger than the third threshold value, the local area is determined as a character area. Eighth step; And When the average gradation value obtained in the third step is not larger than the first threshold value, even the cumulative maximum value is larger than the second threshold value and the directionality is smaller than the third threshold value, the local area is a binary image area. 9th step to determine Phase separation method of the scan image comprising a. The method according to claim 1 or 2, Even the maximum system value of the second step is, Even maximum value (i, j) = MAX (| A-I |, | B-J |, | C-K |, | D-L |, | E-M |, | F-N |, | G-O |, | H-P |), Even maximum value (i, j) = MAX (| A-E |, | B-F |, | C-G |, | D-H |), or Even the maximum system is equal to the value (i, j) = MAX (| A-I |, | E-M |) Determined by any one of the equations, Even the minimum system value of the second step is Even minimum (i, j) = MIN (| A-I |, | B-J |, | C-K |, | D-L |, | E-M |, | F-N |, | G-O |, | H-P |), Even minimum value (i, j) = MIN (| A-E |, | B-F |, | C-G |, | D-H |), or Even the minimum system is equal to the value (i, j) = MIN (| A-I |, | E-M |) The method for separating the scan image of the scan image, characterized in that determined by any one of the equations. I, j means the i-th horizontal j-th pixel in this local area, and A, B, C, D, E, F, G, H, I, J, K, L, M, N , O, P means the peripheral pixels of the vertical i-th horizontal j-th pixel.) The method according to claim 1 or 2, Even the cumulative maximum total value of the third step, A phase separation method of a scanned image, characterized by the following equation. Where n and m represent the nth and mth local areas when the whole image is divided into N vertical and M local areas, and i, j is the local area when the size of this local area is row I and column J. It represents the vertical i-th horizontal j-th pixel in the area.) The method according to claim 1 or 2, The direction value of the third step, A phase separation method of a scanned image, characterized by the following equation. The method according to claim 1 or 2, The average gray level value of the third step is A phase separation method of a scanned image, characterized by the following equation. (However, P (i, j) means the gradation value of the i, j pixel.) In order to separate the scanned image, the apparatus for separating a scanned image having a processor, A first function of dividing an input scan image into local regions; A second function of obtaining a maximum system even value and a minimum system even value of each pixel included in the divided local region; A third function of obtaining a cumulative maximum even value, a direction value, and an average even value of each local area by using the maximum even value and the minimum even value; A fourth function of dividing each local area into a blank area, a continuous gray level image area, a character area, or a binary image area by using the obtained cumulative maximum even value, direction value, and average gray level value; And A fifth function of correcting an error by performing a post-processing process on the separated areas A computer-readable recording medium having recorded thereon a program for realizing this. The method of claim 7, wherein The fourth function is, A sixth function of discriminating the local area as a blank area when the average gradation value obtained by the third function is greater than a first threshold value; A seventh function of discriminating the local area as a continuous gradation image area when the average gradation value obtained by the third function is not greater than the first threshold value, and even the cumulative maximum sum value is not larger than the second threshold value; When the average gradation value obtained by the third function is not greater than the first threshold value, even if the cumulative maximum system value is greater than the second threshold value and the directionality is greater than the third threshold value, the local area is determined as a character area. Eighth function; And When the average gradation value obtained by the third function is not greater than the first threshold value, even the cumulative maximum system value is larger than the second threshold value and the directionality is smaller than the third threshold value, the local area is a binary image area. 9th function to determine A computer-readable recording medium having recorded thereon a program for realizing this.