JP4047356B2 - Image processing device - Google Patents

Description

  The present invention relates to an image processing apparatus used for an image forming apparatus such as a color digital copying machine, a color printer, and a facsimile. More specifically, the present invention detects whether a specific area of input image data is an achromatic pixel and appropriately The present invention relates to an image processing apparatus that performs accurate image processing.

  In recent years, there has been a further demand for higher performance and higher quality image processing apparatuses such as color digital copying machines, color printers, and facsimiles. However, when detecting color (chromatic) pixels or black (achromatic) pixels in a document image input from an image input device such as a scanner, there is an RGB reading shift caused by digital sampling or mechanical accuracy. Exists. For this reason, conventionally, correction is performed by performing a smoothing process so that there is no positional deviation, or pattern matching considering positional deviation is performed. In addition, when performing color determination, a pixel extraction method has been proposed in order to accurately determine chromatic / achromatic pixels.

  For example, in the “image processing apparatus” disclosed in Japanese Patent Laid-Open No. 5-236287, a plurality of lines of RGB multivalued image data are stored and smoothed to correct image data deviation. Here, the threshold value of the pixel to be extracted is switched according to light and dark when the image is extracted and binarized.

  In addition to the above publication, Japanese Patent Laid-Open No. 5-300390 implements accurate image extraction using nonlinear data. Further, in the “image processing apparatus” disclosed in Japanese Patent Laid-Open No. 5-244420, color pixel extraction is performed by determining the hue of image data. In addition, the “image processing apparatus” disclosed in Japanese Patent Laid-Open No. 9-247481 is not related to misregistration, but the color (chromatic) halftone dot or black (achromatic) in the plane of c, m, y. The halftone dot is determined.

JP-A-5-236287 JP-A-5-300390 JP-A-5-244420

  However, in the conventional technique as described above, a plurality of RGB multi-valued image data are stored and smoothed for correction by looking at the number of bits of the multi-value data + the number of reference lines. , Compared with binary data, a very large amount of memory is required, and even if a pattern that takes into account the reading position deviation is created, there is a problem that erroneous determination due to local fluctuations is likely to occur. .

  The present invention has been made in view of the above, and it is an object of the present invention to achieve achromatic color determination and reduce memory capacity.

In order to achieve the above object, in the image processing apparatus according to claim 1, any one of C, M, Y, R, G, B, BK, and W is used as the pixel of the input image input from the outside. Hue splitting means for determining whether each pixel is present, and representing each pixel of C, M, Y, R, G, B, BK, and W as a result of the determination with a 3-bit value (c, m, y) And an n × m matrix (n and m are integers of 1 or more), which is a predetermined area in the input image including the pixels, counts the number of 1 for each (c, m, y), If the minimum value of the counted values is a black pixel, and the maximum value−minimum value is equal to or less than the first predetermined value, the pixel to be processed in the n × m matrix is determined to be an achromatic pixel region. Pixel determination means.

Further, in the image processing apparatus according to claim 2, the achromatic pixel determination means sets the processing target pixel as an achromatic pixel area when the minimum value is equal to or greater than a second predetermined value. It is determined .

Further, in the image processing apparatus according to claim 3, wherein the achromatic pixel determination hand stage, the processing object pixel whether the achromatic pixel region, between maximum and minimum values of the RGB data It is determined according to the difference.

Further, in the image processing apparatus according to claim 4, wherein the achromatic pixel determination hand stage is to set each threshold for each hue, whether the target pixel is achromatic pixel regions, each It is determined according to a threshold value.

In the image processing apparatus according to claim 5 , the achromatic pixel determination unit calculates the first predetermined value and the second predetermined value when detecting a plurality of achromatic pixels. It can be changed .

Further, in the image processing apparatus according to claim 6 , the achromatic pixel determining means detects a plurality of achromatic pixels, determines whether the input image is a color image or a black and white image, and It is determined whether or not the specific area is processed as a black character.

As described above, according to the image processing apparatus of the present invention (claims 1 to 3 ), y and mc planes are developed, and the number is counted for each plane in the n × m matrix, and the minimum By correcting the reading position deviation by determining the value as black, achromatic color determination is realized, and processing is performed after binary data, so RGB reading deviation is reduced with a very small memory capacity compared to multi-value data. Therefore, the memory capacity can be reduced.

Further, according to the image processing apparatus of the present invention (claim 4 ), the hue is determined in order to accurately determine black, and the achromatic determination is performed for each hue. Therefore, the determination is switched according to the hue region. This enables high-precision color extraction.

Further, according to the image processing apparatus of the present invention (Claims 5 and 6 ), when performing a plurality of chromatic / achromatic determinations, processing is performed until the middle of the determination, and a 3-bit plane is counted independently. Since the threshold value can be switched, an increase in memory can be reduced.

  Hereinafter, embodiments of an image processing apparatus according to the present invention will be described in detail with reference to the accompanying drawings.

[Configuration and operation of image processing apparatus]
FIG. 1 is a block diagram showing a schematic configuration of an image processing apparatus according to Embodiment 1 of the present invention, in which image data is read from a document, the image data (analog signal) is converted into digital data and output. A reading unit 101, an image processing unit 102 that performs various correction processes on image data (digital data) read by the document reading unit 101, and performs document recognition such as line drawing recognition and color determination, and from the image processing unit 102 And an image recording unit 103 that records an image on a recording sheet based on the image data.

  Here, the original reading unit 101 reads R (red), G (green), and B (blue) color image data (hereinafter referred to as RGB data), and the image processing unit 102 performs RGB processing. The data is color-converted into four color image data (hereinafter referred to as CMYBk data) of C (cyan), M (magenta), (yellow), and Bk (black), and the image recording unit 103 based on the CMYBk data. In the following description, it is assumed that a color image is output on recording paper.

  FIG. 2 is a block diagram showing the internal configuration of the image processing unit 102 in FIG. 1. When RGB data (digital data) is input from the document reading unit 101, the gray balance of the RGB data is corrected and the reflectance data is corrected. Based on the RGB data input from the document reading unit 101, the RGB γ correction unit 201 that converts (RGB data) into density data (RGB data), and the next RGB filter unit 204 determines whether it is a character area or a picture area. Output a C / P signal to the RGB filter unit 204 and output a B / C signal to the RGB filter unit 204 by determining whether the original region is a chromatic region or an achromatic region. , And a delay unit 203 that delays the RGB data in order to synchronize with the output result of the document recognition unit 202, and MTF correction for the RGB data. An RGB filter unit 204 for performing, a color correction unit 205 for converting RGB data into CMY data by primary masking, and the like; a UCR unit 206 for generating Bk data by performing UCR (additional color removal) processing on a common part of CMY data; , A scaling unit 207 that performs enlargement / reduction or equal magnification processing in the main scanning direction, a CMYBk filter unit 208 that performs smoothing processing and sharpening processing, and CMYBkγ that performs γ correction according to the frequency characteristics of the image recording unit 103. The correction unit 209 includes a gradation processing unit 210 that performs quantization such as dither processing and error diffusion processing.

  The C / P signal output from the document recognition unit 202 is a 2-bit signal. The C / P signal is “3” to indicate a character area, “1” is a character on the picture, and “0” is a picture area. Indicates. The C / P signal is cascade-connected to the RGB filter unit 204, the color correction unit 205, the UCR unit 206, the scaling unit 207, the CMYBk filter unit 208, the CMYBkγ correction unit 209, and the gradation processing unit 210, and is synchronized with the image data. Then, the signal IMG is output.

  The logic of the B / C signal (1-bit signal) indicates an achromatic region with H and a chromatic region with L. The B / C signal is cascade-connected to the RGB filter unit 204, the color correction unit 205, and the UCR unit 206, and is output in synchronization with the image data.

  The RGB filter unit 204 is a filter that performs MTF correction on RGB data, and is configured by an N × N matrix. When the C / P signal is “3”, the sharpening process is performed. When the C / P signal is “0”, the smoothing process is performed. When the C / P signal is “1”, the input data is not processed and is output as it is.

  The UCR unit 206 is for improving the color reproduction of the image data. The UCR (additional color removal) process is performed on the common part of the CMY data input from the color correction unit 205 to generate Bk data. Output. Here, when the C / P signal is other than “3”, Bk generation of skeleton black is performed. When the C / P signal is “3”, full black processing is performed. Further, when the C / P signal is “3” and the B / C signal is H, the data of C · M · Y is erased. This is because black characters are represented only by the black component. The output signal IMG is a field sequential color that outputs one color among C, M, Y, and Bk. That is, full-color (four-color) data is generated by reading the document four times.

  The CMYBk filter unit 208 performs smoothing and sharpening processing using an N × N spatial filter according to the frequency characteristics of the image recording unit 103 and the C / P signal.

  The CMYBkγ correction unit 209 changes and processes the γ curve according to the frequency characteristics of the image recording unit 103 and the C / P signal. When the C / P signal is “0”, the γ curve that faithfully reproduces the image is used, and when the C / P signal is other than “0”, the γ curve is raised to enhance the contrast.

  The gradation processing unit 210 performs quantization such as dither processing according to the gradation characteristics of the image recording unit 103 and the C / P signal. When the C / P signal is “0”, gradation-oriented processing is performed, and when the C / P signal is other than “0”, resolution-oriented processing is performed.

  As is apparent from the configuration of each part of the image processing unit 102, in the image processing unit 102, when the pattern processing (C / P signal = 0), the RGB filter unit 204 performs smoothing processing, and the UCR unit 206 The CMYBkγ correction unit 209 selects a curve that emphasizes linearity (gradation), and the CMYBk filter unit 208 and the gradation processing unit 210 perform processing that emphasizes gradation.

  On the other hand, when character processing (C / P signal = 3), the RGB filter unit 204 performs enhancement processing, the UCR unit 206 performs full black processing, and the CMYBkγ correction unit 209 selects a curve that emphasizes contrast, The CMYBk filter unit 208 and the gradation processing unit 210 perform processing with an emphasis on resolution.

  Also, CMY data is not printed during CMY processing except Bk as black character processing (C / P signal = 3 and B / C signal = H). This is to avoid coloring around black characters due to misalignment. Further, the RGB filter of the Bk data at this time may be made stronger and clearer than that of the color character.

  Further, in the case of pattern processing (C / P signal = 1), the RGB filter unit 204 performs weak enhancement processing or through processing for outputting input data as it is, UCR unit 206 performs full black processing, and CMYBkγ correction unit 209. Then, a curve that emphasizes contrast is selected, and the CMYBk filter unit 208 and the gradation processing unit 210 perform processing that emphasizes resolution. Here, processing such as black character processing is not performed.

  As described above, the image processing unit 102 can perform three types of processing: a pattern, a character edge, and a character on the pattern.

  FIG. 3 is a block diagram illustrating a detailed configuration of the document recognition unit 202. The document recognition unit 202 is roughly divided into a line drawing recognition unit 301 that detects line-likeness and a color determination unit 302 that determines whether a specific area of the document is chromatic or achromatic. Here, a case where the reading density of the document reading unit 101 is about 400 dpi will be described as an example.

  The line drawing recognition unit 301 outputs a C / P signal, and its output logic outputs “3” when it is the edge of the line drawing, and outputs “1” when it is the edge of the line drawing on the pattern. In other cases, “0” is output.

  The line drawing recognition unit 301 includes a monochrome conversion unit 303, a Laplacian unit 304, pattern matching units 305A and 305B, isolated point removal units 306A and 306B, pixel density conversion units 307A and 307B, and a page, as illustrated. The memory 308A, 308B, selectors 309A, 309B, isolated block removal units 310A, 310B, and expansion units 311A, 311B are configured. The line drawing recognition unit 301 outputs C / PA and C / PB. When CPA and B are (L, L), the C / P signal is “0” and (L, H). When the C / P signal is “1”, (H, H), the C / P signal is “3”, and C / PA and C / PB are called C / P signals.

  Further, the document recognition unit 202 determines whether the document read by the document reading unit 101 is a color document or a monochrome document. In the automatic color selection mode, it is determined whether the document is a color document or a monochrome document during the first scan (Bk) image formation. If it is determined that the document is a black and white document, scanning is completed once. On the other hand, if it is determined that the document is a color document, scanning is performed four times.

  The color determination unit 302 outputs a B / C signal, and its output logic outputs L when it is a chromatic region and H when it is an achromatic region. The output result is a signal in which 4 × 4 pixels correspond to one pixel. In the following, the unit of the output result is one block. The color determination unit 302 includes a color determination circuit 312, a page memory 313, and a selector 314 as shown in the figure.

  In the above configuration, the operation of each unit of the line drawing recognition unit 301 will be described in detail.

  The RGB data input to the line drawing recognition unit 301 is first converted into luminance data by the monochrome conversion unit 303 to be a monochrome signal. However, even if it is not luminance data, the darkest data among RGB data may be selected and used as a monochrome signal, or G data may be used as luminance data and may be used as a monochrome signal. In any case, the output data output from the monochromator 303 is dark when the number is large and light when it is small.

  A Laplacian unit 304 extracts a line drawing edge and simultaneously detects a white area and a black area. By detecting the white area, the line drawing data (thin line candidate) on the white ground is obtained.

Here, the operation of white area detection will be described using the symbolized matrix of FIG. For example, when the white area matrix is 3 × 3, the following is obtained.
((a ₀₀ <thw) and (a ₀₁ <thw) and (a ₀₂ <thw)
and (a ₁₀ <thw) and (a ₁₁ <thw) and (a ₀₂ <thw))
or ((a ₁₀ <thw) and (a ₁₁ <thw) and (a ₁₂ <thw)
and (a ₂₀ <thw) and (a ₂₁ <thw) and (a ₂₂ <thw))
or ((a ₀₀ <thw) and (a ₁₀ <thw) and (a ₂₀ <thw)
and (a ₀₁ <thw) and (a ₁₁ <thw) and (a ₂₁ <thw))
or ((a ₀₁ <thw) and (a ₁₁ <thw) and (a ₂₁ <thw)
and (a ₀₂ <thw) and (a ₁₂ <thw) and (a ₂₂ <thw))

  When the peripheral data including the target pixel is smaller than the threshold thw, it is determined as a white area candidate. Here, different values are used for the thick line and the thin line. For the thick line white area candidate, a general white background value is set. The fine line white area candidate is set to a value (a value close to white) that is slightly lower than a general white background value. The reason for making the white line candidate for the fine line closer to white is to avoid the highlight (brighter one) of the pattern (the halftone dot of the printed matter or the copying machine line) becoming the white area candidate.

  This pattern shows an example of an orthogonal pattern, but a diagonal pattern may be added.

Further, Laplacian is obtained from the white area candidate as follows in order to calculate the white area.
x = (a ₂₂ × 2) − (a ₂₁ + a ₂₃ ) × i
x = ((a ₂₂ × 4) − (a ₁₁ + a ₁₃ + a ₃₁ + a ₃₃ )) × i / 2 + x
x = (a ₂₂ × 2) − (a ₁₂ + a ₃₂ ) + x

Here, i is a difference between the MTF of the main scanning and the sub-scanning, and a weighting coefficient for correcting at the time of scaling. If the value of x at this time is within a certain range (N), a white region is set. It is described as an expression as follows.
-N <x <N
Here, the thick line threshold and the thin line threshold may or may not be separated.

  In this way, the white area for fine lines and the white area for thick lines are calculated. In this way, the small dots and line patterns on the highlight side on the pattern are removed so as not to be extracted.

  Next, correction of the white area for thick lines will be described. For example, in the case of an image of black and white reversed characters (white is a character and the surrounding is black), in the case of an optical reading device such as a copying machine, flare (not only a single point of white but the influence of black around it) etc. , White data may be blacker than usual. For this reason, the following correction is performed.

For example, when the white area matrix is 3 × 3, the following is obtained.
((a ₀₀ <thw) and (a ₀₁ <thw) and (a ₀₂ <thw)
and (a ₁₀ <thw) and (a ₁₁ <thw) and (a ₀₂ <thw)
and (a ₂₀ <thw) and (a ₂₁ <thw) and (a ₂₂ <thw))

  Using this as a corrected white area candidate, a corrected white area is calculated using the Laplacian described above. Here, thw is a value from black to a thick line, and N is set to be smaller than the value of the white area for thick lines described above. The reason for reducing N is to extract stable data with a small amount of change in white data. The result of the corrected white area extracted here is corrected to the thick line white area described above to obtain a thick line corrected white area. That is, if it is a corrected white area or a white area for thick lines, it becomes a corrected white area for thick lines. Here too, the small dots and line patterns on the highlight side of the pattern are removed so as not to be extracted.

Next, by detecting the black region, the extracted data of the line drawing on the black region is obtained. Here, the operation of black region detection will be described using the symbolized matrix of FIG. For example, assuming that the black area matrix is 3 × 3, the following occurs.
((a ₀₀ <thb) and (a ₀₁ <thb) and (a ₀₂ <thb)
and (a ₁₀ <thb) and (a ₁₁ <thb) and (a ₀₂ <thb))
or ((a ₁₀ <thb) and (a ₁₁ <thb) and (a ₁₂ <thb)
and (a ₂₀ <thb) and (a ₂₁ <thb) and (a ₂₂ <thb))
or ((a ₀₀ <thb) and (a ₁₀ <thb) and (a ₂₀ <thb)
and (a ₀₁ <thb) and (a ₁₁ <thb) and (a ₂₁ <thb))
or ((a ₀₁ <thb) and (a ₁₁ <thb) and (a ₂₁ <thb)
and (a ₀₂ <thb) and (a ₁₂ <thb) and (a ₂₂ <thb))

  When the peripheral data including the target pixel is smaller than the threshold thb, it is determined as a black region candidate. For the black region candidate, a general black value (in other words, a density to be emphasized as a character) is set. This pattern shows an example of an orthogonal pattern, but a diagonal pattern may be added.

Further, Laplacian is calculated from the black region candidates as follows to calculate the black region.
x = (a ₂₂ × 2) − (a ₂₁ + a ₂₃ ) × i
x = ((a ₂₂ × 4) − (a ₁₁ + a ₁₃ + a ₃₁ + a ₃₃ )) × i / 2 + x
x = (a ₂₂ × 2) − (a ₁₂ + a ₃₂ ) + x

  Here, i is a difference between the main scanning and sub-scanning MTFs, and a weighting coefficient for correction at the time of zooming, and may be the same as the above-described white region extraction.

If the value of x at this time is within a certain value (N), a black region is set. It is described as an expression as follows.
-N <x <N
This result is defined as a black region. As a result, the halftone dots and line patterns on the shadow side on the pattern are removed so as not to be extracted.

The extraction of the edge amount is based on the following formula.
x = (a ₂₂ × 2) − (a ₂₁ + a ₂₃ ) × i
x = ((a ₂₂ × 4) − (a ₁₁ + a ₁₃ + a ₃₁ + a ₃₃ )) × i / 2 + x
x = (a ₂₂ × 2) − (a ₁₂ + a ₃₂ ) + x

  Here, i is a selective coefficient, and is a coefficient that reduces the gate scale when designing hardware, for example, 1, 1.115, 1.25, 1.375, 1.5, 1. 625, 1.175, 1.875, 2 (fixed point arithmetic). By doing so, blurring of MTF (optical system and traveling system) of main scanning and sub-scanning is corrected.

  In general, the MTFs for main scanning and sub-scanning are different, and the magnification for sub-scanning is changed by changing the reading area (speed) of the reading device. Therefore, the MTF differs depending on the magnification for sub-scanning. However, in this example, as shown in FIG. 2, the main scanning scaling (magnification unit 207) is disposed after the document recognition unit 202, so there is no particular concern. Further, when the sub-scanning magnification is large, for example, 200%, the matrix can be selected so as to obtain the edge amount as follows.

x = (a ₂₂ × 2) − (a ₂₁ + a ₂₃ ) × i
x = ((a ₂₂ × 4) − (a ₁₁ + a ₁₃ + a ₃₁ + a ₃₃ )) × i / 2 + x
x = (a ₂₂ × 2) − (a ₁₂ + a ₃₂ ) + x

In this way, the sub-scanning scaling process is supported.
As described above, the Laplacian unit 304 outputs the white area signal, the black area signal, and the edge amount. A white area signal (for thick lines and thin lines) indicates a white area with L, and a black area signal indicates a black area with H.

  FIG. 5 is a cross-sectional view of a line drawing, and is a conceptual diagram showing a relationship between a white area, a black area, and a threshold value. In the figure, THB is a black area threshold, Thw1 is a white line thin line threshold, Thw2 is a white line thick line threshold, and Thw3 is a white area correction threshold. FIG. 6 is an explanatory diagram showing the relationship of the edge amount (x).

Next, the operation of the pattern matching units 305A and 305B will be described.
The pattern matching unit 305A extracts a white area around the black area. Here, the white area pattern (W) is a signal for a white area for correction thick line, and the black pattern (K) is a black area signal. An example pattern is as follows (7 × 7).

(k ₁₂ AND k ₁₃ AND k ₁₄ AND k ₁₂ AND k ₂₃ AND k ₂₄ AND k ₃₂ AND k ₃₃ AND k ₃₄ AND ((w ₅₂ AND w ₅₃ AND w ₅₄ ) or (w ₆₂ AND w ₆₃ AND w ₆₄ )
or (w ₁₂ AND w ₁₃ AND w ₁₄ ) or (w ₀₂ AND w ₀₃ AND w ₀₄ )))
or
(k ₂₁ AND k ₃₁ AND k ₄₁ AND k ₂₂ AND k ₃₂ AND k ₄₂ AND k ₂₃ AND k ₃₃ AND k ₄₄ AND ((w ₂₅ AND w ₃₅ AND w ₄₅ ) or (w ₂₆ AND w ₃₆ AND w ₄₆ )
or (w ₂₁ AND w ₃₁ AND w ₄₁ ) or (w ₂₀ AND w ₃₀ AND w ₄₀ )))

  In the above example, only the horizontal component and the vertical component are shown, but the diagonal component pattern is also extracted. In this way, the white area on the black area is extracted. Since there are many black areas, it is possible to extract a black area line drawing without erroneously recognizing a halftone dot as a line drawing.

Alternatively, the coding may be performed using the magnitude relationship among the black area, the white area for thick line correction, and the thin line white area. As an example of encoding, a black area is described as B, a white area for thick line correction as W1, and a thin line white area as W2. In this case, if it is not coded, it becomes 3 bits × n lines, but if it is coded as follows, it becomes 2 bits × n lines.
When B → Code “1”
W2 → Code “2”
When W1 and not W2 → Code “3”
When neither B nor W1 nor W2 → Code “0”

  Since the code is “0” to “3”, it can be expressed by 2 bits. When the code is expanded, the reverse process may be performed. Of course, the magnitude relationship does not have to be fixed, and it should be possible to replace it.

  The processing flow is as shown in the flowcharts of FIGS. When the target pixel is set as shown in FIG. 9, the process is executed in the main scanning direction as shown in FIG. The target pixel is set to +1 in the sub-scanning direction, and the process is performed again in the main scanning direction.

  Here, the pattern matching processing (pattern matching processing in FIG. 7) will be described with reference to the flowchart in FIG. Pattern matching is performed to determine whether or not the pattern matches the above-described pattern (S801). If the pattern matches, the output PM1 (see FIG. 2) outputs H (on) (S802). If the pattern does not match, the output PM1 is L (off). Is output (S803).

  The pattern matching unit 305A extracts the edge of the thick line portion of the line drawing by the above processing.

  The pattern matching unit 305B detects a thin line. A fine line means a character and a line drawing having a line width of 1 mm or less. Here, the black pattern (k) is H when the black region or the edge amount is larger than the threshold value THRB (see FIG. 6). In the white pattern (w), H is defined as a white area for fine lines or an edge amount smaller than the threshold value THRW (the absolute value is large because it is a negative component). It should be noted that the magnification may be changed with the original, the original type (color, black and white, printed photograph, photographic paper photograph, copy original, map, etc.), adjustment key, or the like. That is, the reason for correcting with the edge amount component is to increase the contrast of the thin line.

An example of the fine line pattern is as follows (7 × 7).
((w ₂₂ AND w ₂₃ AND w ₂₄ ) or (w ₀₂ AND w ₀₃ AND w ₀₄ )
AND w ₁₂ AND w ₁₃ AND w ₁₄
AND k ₃₂ AND k ₃₃ AND k ₃₄
AND w ₅₂ AND w ₅₃ AND w ₅₄
AND (w ₄₂ AND w ₄₃ AND w ₄₄ ) or (w ₆₂ AND w ₆₃ AND w ₆₄ ))
or
((w ₂₂ AND w ₃₂ AND w ₄₂ ) or (w ₂₀ AND w ₃₀ AND w ₄₀ )
AND w ₂₁ AND w ₃₁ AND w ₄₁
AND k ₂₃ AND k ₃₃ AND k ₄₃
AND w ₂₅ AND w ₃₅ AND w ₄₅
AND (w ₂₄ AND w ₃₄ AND w ₄₄ ) or (w ₂₆ AND w ₃₆ AND w ₄₆ ))
or
((w ₁₂ AND w ₁₃ AND w ₁₄ ) or (w ₀₂ AND w ₀₃ AND w ₀₄ )
AND w ₃₂ AND w ₃₃ AND w ₃₄
AND ((k ₂₂ AND k ₂₃ AND k ₂₄ ) or (k ₄₂ AND k ₄₃ AND k ₄₄ ))
AND (w ₅₂ AND w ₅₃ AND w ₅₄ ) or (w ₆₂ AND w ₆₃ AND w ₆₄ ))
or
((w ₂₁ AND w ₃₁ AND w ₄₁ ) or (w ₂₀ AND w ₃₀ AND w ₄₀ )
AND w ₂₃ AND w ₃₃ AND w ₄₃
AND ((k ₂₂ AND k ₃₂ AND k ₄₂ ) or (k ₂₄ AND k ₃₄ AND k ₄₄ ))
AND (w ₂₅ AND w ₃₅ AND w ₄₅ ) or (w ₂₆ AND w ₃₆ AND w ₄₆ ))

  Although only the horizontal component and the vertical component are shown here, the diagonal component pattern is extracted in the same manner. In this way, when both sides of the black pattern are sandwiched between white patterns, they are extracted as thin line candidates.

  The processing flow is as shown in the flowcharts of FIGS. When the target pixel is set as shown in FIG. 9, the process is executed in the main scanning direction as shown in FIG. The target pixel is set to +1 in the sub-scanning direction, and the process is performed again in the main scanning direction.

  Here, the pattern matching processing of the pattern matching unit 305B will be described with reference to the flowchart of FIG. Note that MFB is a state variable and is 0 at the front end of main scanning. SFB (i) is an array for one line in the main scanning direction, and is a state variable one line before.

  First, the current state variable MFB is compared with the state variable SFB (i) one line before (S11). If MFB <SFB (i), MFB = SFB (i) is set (S12), and the process proceeds to step S12. Otherwise, the process proceeds to step S12.

  In step S12, it is determined whether or not the output PM1 from the pattern matching unit 305A is on. If PM1 is not on (that is, off), the process proceeds to step S18. On the other hand, if PM1 is on, if the value of MFB is larger than 0 in steps S14 to S17, the value is changed. Specifically, if MFB is larger than 8, it is set to 16 (S14, S15), and if MFB is larger than 0 and smaller than 8, it is set to 8.

  In step S18, it is determined whether the area is a white background. Here, the white area is determined as a white area when the white area for fine lines output from the Laplacian unit 304 is a, as follows.

a ₀₀ AND a ₀₁ AND a ₀₂
AND a ₁₀ AND a ₁₁ AND a ₁₂
AND a ₂₀ AND a ₂₁ AND a ₁₁

  If it is determined in step S18 that the region is a white background region, L (off) is output to output 1 and output 2 of the pattern matching unit 305B (S19), MFB = 16 is set (S20), and the process proceeds to step S36.

  On the other hand, if it is determined in step S18 that the region is not a white background region, it is determined whether or not it is a fine line pattern depending on whether or not it matches the fine line pattern described above (S21). L (off) is output to output 1 and output 2 of 305B (S22), and it is determined whether MFB = 0 (S23). If MFB = 0, the process proceeds to step S36, and MFB = 0 must be satisfied. For example, MFB = MFB-1 is set (S24), and the process proceeds to step S36.

  In step S21, if the pattern is a fine line pattern, it is determined whether MFB> 8 (S25). If greater than 8, H (on) is output to the output 1 and output 2 of the pattern matching unit 305B. In S26 and S29, if MFB> 16, MFB = 16 is set. If MFB> 16, the process proceeds directly to step S36.

  If it is not greater than 8 in step S25, it is determined whether MFB = 0 (S30). If MFB = 0, L (off) is output to the output 1 and output 2 of the pattern matching unit 305B. In step S36, if MFB is not 0, L (off) is output to the output 1 of the pattern matching unit 305B, and H (on) is output to the output 2 (S32). MFB = MFB + 4 (However, if MFB is 16 or more, set it to 16) (S33), and in steps S34 and S35, if MFB> 8, set MFB = 8, and MFB> 8. If it is, the process proceeds to step S36.

  In step S36, SFB (i) = MFB is set, and the state variable SFB (i) one line before is updated. Next, in step S37, SFB (i)> SFB (i-1) is determined. This is a comparison between the updated data one line before and the updated data one pixel one pixel before. If the data SFB (i) one line before is large, SFB (i−1) = SFB (i) is set (S38), and the process is terminated.

  The above processing is sequentially performed in the main scanning direction. That is, the state variable MFB sequentially propagates in the direction of arrow 3 in FIG. Then, it propagates in the direction of arrow 1 in step S36, and propagates in the direction of arrow 2 in step S37. From this, it is possible to detect the fine line on the white ground by setting the state variable in the white area determination in step S18 or the pattern matching in step S12, and the halftone dots on the pattern can be erroneously extracted. Disappear. Further, since the state variable is reset by the fine line pattern matching in step S14, it is possible to extract the character chunks well.

  In addition, since the output 1 and the output 2 of the pattern matching unit 305B are output differently depending on the state variable, it is possible to separate and output the characters on the white background and the characters on the halftone dots.

  As is clear from FIG. 11, since the sub-scanning arrow direction is 0 or + (plus) direction, the processing performed for each line (each main scanning line) is performed by using state variables and pattern matching for one line. It is sufficient to provide a necessary number of lines of memory, and this can be easily realized without providing a page memory.

As shown in FIG. 2, the pattern matching unit 305B outputs output 1 to the isolated point removing unit 306A and outputs output 2 to the isolated point removing unit 306B.
The difference between output 1 and output 2 is the difference in state variables. Thus, for example, as shown in FIG. 12, when a character (a, i, u, etc.) is described inside the ruled line frame and the inside of the frame is a halftone dot, the ruled line frame is output 1, It is possible to determine that the output 2 is a thin line, and it is possible to determine that only the output 2 having a large state variable is a thin line.

Next, the isolated point removal units 306A and 306B will be described.
The isolated point removal units 306A and 306B are both composed of the same circuit. The input data of the isolated point removal unit 306A is composed of the output (PM1) of the pattern matching unit 305A and the output (output 1) of the pattern matching unit 305B, and the input data of the isolated point removal unit 306B is the output of the pattern matching unit 305A ( PM1) and the output (output 2) of the pattern matching unit 305B. In the isolated point removal units 306A and 306B, since the line drawing is formed of continuous lines, the isolated points are removed. An isolated point occurs when a halftone dot is mistakenly detected as a line drawing.

If any one of the pattern matching unit 305A and the pattern matching unit 305B is an extraction pattern, the extraction pattern is used. For example, in 4 × 4 pattern matching, if the extraction pattern is 2 or more, the center pixel (may be a ₂₂ or a ₃₃ ) is corrected as the extraction pattern and output H is output (referred to as extraction pattern). This causes expansion (expansion) at the same time as isolated points are removed. As shown in FIG. 3, the outputs of the isolated point removal units 306A and 306B are PM2 and PM3, respectively.

  Next, the pixel density conversion units 307A and 307B will be described. The pixel density conversion units 307A and 307B are the same logic (circuit). Until now, the processing has been performed in units of images. However, since processing is performed in units of blocks (4 × 4), data in units of pixels is converted into units of blocks. Here, although simple 4 × 4 thinning is performed, since the isolated point removing units 306A and 306B substantially perform 4 × 4 expansion, no data is lost.

  Next, the page memories 308A and 308B and the page memory 313 (page memory of the color determination unit 302) will be described. The circuits of the page memories 308A, 308B, and 313 all have the same function. The page memory 308A receives the output result of the pixel density conversion unit 307A, the page memory 308B receives the output result of the pixel density conversion unit 307B, and the page memory 313 inputs the output result of the color determination circuit 312.

  The page memories 308A, 308B and 313 are composed of 1200 dots in the main scanning direction × 1736 lines in the sub scanning direction (about 2 MB), and A3 size and DLT paper (double letter) with a resolution of 16 dots / mm in both the main and sub scanning directions. Size) has a larger size. At the time of the first scan, input data is stored in the page memories 308A, 308B, and 313 in units of blocks (4 × 4 pixels) and simultaneously output via the selectors 309A, 309B, and 314. In the second and subsequent scans, the determination results stored in the page memories 308A, 308B, and 313 at the first scan are output via the selectors 309A, 309B, and 314. That is, since the color determination result and the line drawing extraction processing data in the first scan are used in the second and subsequent scans, it is possible to eliminate variations in the color determination result and the line drawing extraction result (C / P signal) for each scan.

Next, the isolated block removal units 310A and 310B will be described.
The isolated block removal units 310A and 310B have the same function in the same circuit. For example, in a matrix of 5 × 5 blocks, when only the central block is on (H) and the others are off (L), this block is isolated, so the output L is output as off. “on” means an extraction pattern. This removes blocks that are isolated from the peripheral data.

Next, the expanding portions 311A and 311B will be described.
The expanding portions 311A and 311B have the same function in the same circuit. Here, N × N OR processing (expansion) is performed, and then M × M AND processing is performed (reduction). Then, 5 × 5 interpolation processing is performed. MN becomes the expansion amount.

M and N are N> M. Here, the OR process is performed in order to connect an isolated block with an adjacent block or a neighboring block. As an example, an expansion example of a 3 × 3 block (corresponding to 12 × 12 pixels) is shown.
a ₀₀ or a ₀₁ or a ₀₂
or a ₁₀ or a ₁₁ or a ₁₂
or a ₂₀ or a ₂₁ or a ₁₁

Thereafter, AND processing (shrinkage) of 5 × 5 pixels is performed. An example is shown below.
a ₀₀ AND a ₀₁ AND a ₀₂ AND a ₀₃ AND a ₀₄
AND a ₁₀ AND a ₁₁ AND a ₁₂ AND a ₁₃ AND a ₁₄
AND a ₂₀ AND a ₂₁ AND a ₂₂ AND a ₂₃ AND a ₂₄
AND a ₃₀ AND a ₃₁ AND a ₃₂ AND a ₃₃ AND a ₃₄
AND a ₄₀ AND a ₄₁ AND a ₄₂ AND a ₄₃ AND a ₄₄

Since 100 dpi jagged edges remain after that, interpolation processing is performed. FIG. 13 shows an example of interpolation processing. In the figure, a solid line indicates a jagged edge before correction of 100 dpi, and a broken line indicates an output after correction.
For example, in a 5 × 5 matrix: When the line drawing extracted data satisfies the following logical expression, the data of the pixel of interest a ₂₂ is reflected (corrected).
(Pattern 1 and! Pattern 2)
or (pattern 3 and! pattern 4)
Details of the patterns 1, 2, 3 and 4 are as follows. Here! Indicates an indefinite operator.

Pattern 1
(a ₀₀ and a ₀₂ and! a ₀₄ and a ₂₀ and a ₂₂ and! a ₂₄
and! a ₄₀ and! a ₄₂ and! a ₄₄ )
or (! a ₀₀ and a ₀₂ and a ₀₄ and! a ₂₀ and a ₂₂ and a ₂₄
and! a ₄₀ and! a ₄₂ and! a ₄₄ )
or (! a ₀₀ and! a ₀₂ and! a ₀₄ and a ₂₀ and a ₂₂ and! a ₂₄
and a ₄₀ and a ₄₂ and! a ₄₄ )
or (! a ₀₀ and! a ₀₂ and! a ₀₄ and! a ₂₀ and a ₂₂ and a ₂₄
and! a ₄₀ and a ₄₂ and a ₄₄ )

Pattern 2
(a ₁₁ and a ₁₂ and a ₁₃ and a ₂₁ and a ₂₂ and a ₂₃
and a ₃₁ and a ₃₂ and a ₃₃ )

Pattern 3
(! A ₀₀ and! A ₀₂ and a ₀₄ and! A ₂₀ and! A ₂₂ and a ₂₄
and a ₄₀ and a ₄₂ and a ₄₄ )
or (a ₀₀ and! a ₀₂ and! a ₀₄ and a ₂₀ and! a ₂₂ and! a ₂₄
and a ₄₀ and a ₄₂ and a ₄₄ )
or (a ₀₀ and a ₀₂ and a ₀₄ and! a ₂₀ and! a ₂₂ and a ₂₄
and! a ₄₀ and! a ₄₂ and a ₄₄ )
or (a ₀₀ and a ₀₂ and a ₀₄ and a ₂₀ and! a ₂₂ and! a ₂₄
and a ₄₀ and! a ₄₂ and! a ₄₄ )

Pattern 4
(! A ₁₁ and! A ₁₂ and! A ₁₃ and! A ₂₁ and! A ₂₂ and! A ₂₃
and! a ₃₁ and! a ₃₂ and! a ₃₃ )

  By expanding the extraction pattern, the intersections of characters are connected, and line drawing and its surroundings are processed. The pattern matching described above cannot extract the intersection of the crosses, but can be connected by this expansion process. The reason why line drawings and their surroundings are regarded as line drawings is to allow black character processing and spatial filtering to work well.

  This makes it possible to extract a ruled line even if there are halftone dots in the ruled line as in the catalog usage period / specification table. A ruled line including a white background is extracted as a character, and the character on the halftone dot outputs a determination result different from the character on the white ground. Is possible.

[Another example of the pattern matching unit 305B of the line drawing recognition unit 301]
Here, the output condition of the pattern matching unit 305B of the line drawing recognition unit 301 shown in FIG. 3 is changed. The basic configuration and operation are the same as described above, and only different parts will be described here.

  In the above description, in the fine line pattern matching performed by the pattern matching unit 305B, output 1 and output 2 are set according to the state variables. However, here, a setting condition for output 2 is further added to display characters (ruled lines) on the pattern. It is to be able to extract.

  FIG. 14 shows a flowchart of the second pattern matching process (pattern matching unit 305B). Since the same reference numerals as those in the flowchart shown in FIG. 10 indicate common processes, only different portions will be described here.

  In step S21, it is determined whether or not it is the above-described thin line pattern. If it is not the thin line pattern, it is determined whether or not it matches the following thin line pattern 1 (S40).

  The fine line pattern 1 is the same as the fine line pattern described above, but may not be the same. Here, the black pattern (k) is H when the black region or the edge amount is larger than the threshold value THRB (see FIG. 6). In the white pattern (w), H is defined as a white area for fine lines or an edge amount smaller than the threshold value THRW (the absolute value is large because it is a negative component). That is, the reason for correcting with the edge amount component is to increase the contrast of the thin line. At least one of THRB and THRW is set to a value that is easier to extract than fine line pattern matching. However, when the pattern matching pattern between the fine line pattern and the fine line pattern 1 is different, the fine line pattern is made easier to extract.

  If it does not coincide with the thin line pattern 1 in step S40, L (off) is output to the output 1 and output 2 of the pattern matching unit 305B (S22), and it is determined whether MFB = 0 (S23), If MFB = 0, the process proceeds to step S36, and if not MFB = 0, MFB = MFB-1 is set (S24), and the process proceeds to step S36.

  On the other hand, if it matches with the fine line pattern 1 in step S40, it is determined whether MFB> 8 (41). If the state variable MFB is larger than 8, L (off) is output to the output 1 of the pattern matching unit 305B. ), H (on) is output to output 2 (S43), MFB = MFB-1 is set (S24), and the process proceeds to step S36. If the state variable MFB is not greater than 8, it is determined whether MFB = 0 (S42). If MFB = 0, the process proceeds to step S32. If MFB = 0, the pattern matching unit 305B L (off) is output to output 1 and output 2 (S22), and it is determined again whether MFB = 0 (S23). If MFB = 0, the process proceeds to step S36, and MFB = 0 must be satisfied. For example, MFB = MFB-1 is set (S24), and the process proceeds to step S36.

Further, fine line pattern matching and fine line pattern matching 1, threshold values THRW and THRB may be coded using a magnitude relationship.
As an example of coding, THRW and THRB of the thin line pattern are THRW and THRB, respectively, THRW and THRB of the thin line pattern 1 are THRW1 and THRB1, respectively, and the magnitude relationship is THRW <THRW1 <THRB1 = THRB
Then, in this case, if it is not coded, it becomes 4 or 3 bits × n lines, but if it is coded as follows, it becomes 2 bits × n lines.

P <THRW → Code “0”
THRW <P <THRW1 → Code “1”
THRW1 <P <THRB → Code “2”
THRB <P → Code “3”

  Here, P is an edge amount. Since the code is “0” to “3”, it can be expressed by 2 bits. When the code is expanded, the reverse process may be performed. Of course, the magnitude relationship does not have to be fixed, and it should be possible to replace it.

  Thus, it is possible to switch between a white ground pattern and a halftone dot or color ground pattern using the state variable (MFB), and the state variable can be used in common.

  Further, if it is possible to extract characters on the halftone dot well, when extracting the character on the halftone dot (S40 in the flowchart of FIG. 14), it is not necessary to refer to the state variable (determination of S42). Always match). The character on the halftone dot and the character on the white ground may be separated by such a method.

Therefore, even if there are halftone dots in the ruled line as in the usage instruction / specification table of the catalog, the ruled line can be extracted well.
Further, since the ruled line including the white background is extracted as a character and the character on the halftone dot is determined separately from the character on the white background, the accuracy is further improved as compared with the first embodiment. It is possible to distinguish between the characters on the white background and the characters on the halftone dots and perform different processing.

  In FIG. 11, there are only three propagation directions of P (i, J + 1), P (i + 1, J), and P (i + 1, J-1). In particular, regarding the direction of P (i + 1, J-1). , -1 as well as -2, -3, etc. should be added to eliminate the main scanning directionality of the state variable propagation direction.

  Furthermore, in an apparatus that performs all image data using a page memory, it is needless to say that the propagation direction of the state variable is omnidirectional (360 degrees).

  A detail emphasis effect by unsharp masking shown in FIG. 15 is applied. In the figure, (a) shows a main signal to be processed, (b) shows an unsharp signal, (c) shows an unsharp mask signal, and (d) shows a detail-enhanced signal. Correction is performed based on these edge characteristic examples. In the second embodiment, the Laplacian unit 304 corrects the edge amount using the unsharp mask signal shown in FIG. 15C, but the correction is performed using the detail emphasizing signal and other signals shown in FIG. 15D. May be.

  Further, when the pattern matching unit 305A picks up black (outline) on the white background, characters on halftone dots (shaded) are not extracted. The pattern matching unit 305B separately extracts the ruled line on the white ground and the ruled line on the halftone dot or color ground. For example, complicated characters such as “call” are extracted by the pattern matching unit 305B.

  In the example described above, the upper limit value of the state variable has been described as 8 (characters on a halftone dot) and 16 (characters on a white background), but any number may be used.

  The method of separating the characters on the halftone dots from the characters on the white ground is that there are shading in the ruled lines. To avoid false detection of the characters in the ruled lines, if the ruled lines are thin, the characters may be close. This is because, as the width of the ruled line becomes thicker, the possibility that the characters are nearby decreases.

  According to the above-described example, it is possible to separately extract small characters, line drawings, characters with a large number of strokes on white ground, and characters on halftone dots. Since the reflection direction in the sub-scanning direction is one direction, the raster scanning method is particularly suitable for hardware and can be easily reflected in image data.

  The above image processing apparatus is an algorithm for detecting the edge of a line drawing, and does not specifically detect halftone dots peculiar to printed matter. This is especially effective for manuscripts.

  In the extracted pattern, small area misjudgment is removed by removing isolated points in pixel units, and then isolated blocks are removed in a wide range in large block units (4 × 4) units, so that misjudgment can be removed well. . Further, since the coarse pixel density of the block unit is converted into the original pixel density, there is no coarse pixel of the block unit.

  In addition, if simple expansion is performed in the expansion sections 311A and 311B, the isolated region only becomes large. However, as in this embodiment, when the expansion amount is X, X = MN, M Since the pixels are expanded and then reduced by N pixels, since X <M, isolated regions can be connected. Further, since the expansion is performed with a coarse density, in other words, the expansion is performed with the coarse density (in blocks), so that the amount of hardware can be reduced.

  Further, since the result of the first scan is also used after the second scan, the line drawing determination result and the color determination result always match, so that the processing can be performed without variation. Furthermore, the data stored in the memory is not the final result but the data whose processing density has become rough (roughened) during the data processing, so that the amount of data stored in the memory can be reduced.

  Further, instead of storing both the line drawing determination result and the color determination result in the page memories 308A, 308B and 313, for example, only the line drawing determination result may be stored, and the color determination result may be used for each scan. . As a result, the memory capacity is 2 MB × 2 = 4 MB, so that it can be easily configured using a commercially available 4 MB memory. Further, even if the page memory 308A is 2 MB, the page memories 308B and 131 are not 4 × 4 blocks, but 8 × 4 blocks, and the memory capacity of the page memories 308B and 131 is 1 MB, the total memory capacity is 4 MB (2 MB × 2 MB × 2). 2 + 1 MB).

  Further, instead of storing all the determination results in the memory, only those having large variations for each scan may be stored in the memory.

[Another example of the color determination unit 302]
FIG. 16 is a block diagram illustrating a configuration of a color determination unit, which is a main part of the present invention, according to the embodiment of the present invention. The color determination unit 302 includes a hue division unit 1601 as a hue division unit configured and operated as described later, line memories 1602 to 1604 that store five lines of outputs c, m, and y of the hue division unit 1601, and a later-described unit. The color pixel determination unit 1605 is configured as shown in FIG. 17 and has a function of an achromatic pixel determination unit that determines whether input image data is a black pixel or a color pixel.

  17 and 18 are block diagrams showing the internal configuration of the color pixel determination unit 1605 in FIG. The color pixel determination unit 1605 includes a counting unit 1701 and a counting unit 1702 that independently counts the c, m, and y planes, and the pixels determined by the color division unit 1601 are 5 × 5. A pattern matching unit 1703 that performs predetermined pattern matching, which will be described later, on a pixel (color pixel) portion where one or all of the pixels are not 0, and a black pixel determination unit 1704 that determines a black pixel based on the output of the count unit 1701. And a color pixel determination unit 1705 that determines a color pixel based on outputs of the count unit 1702 and the pattern matching unit 1703.

  Further, as shown in FIG. 18, the color pixel determination unit 1605 receives the output value of the black pixel determination unit 1704, and blocks the blocking unit 1801 for blocking and the data blocked by the blocking unit 1801. An isolated point removing unit 1802 that removes as an isolated point when there is no black pixel block in an adjacent pixel of the pixel, and an expansion unit that performs an expansion process on 3 block pixels when there is a color pixel block 1803, an output value of the color pixel determination unit 1705, and a block forming unit 1804 for blocking, and a continuous count unit for detecting the continuity of the color pixel block and determining whether the document is a color document or a monochrome document 1805.

  Next, the operation of the color determination unit configured as described above will be described. The hue division unit 1601 divides the signals into R, G, B, C, M, Y, Bk, and W signals. As an example of color division, the boundary of each color is obtained, and the difference between the maximum value and the minimum value of RGB is defined as RGB difference, and is as follows. In this example, the RGB data becomes black as the number increases.

(1) RY hue region boundary (ry)
Ry = 1 when R-2 * G + B> 0, ry = 0 when they do not match
(2) Y-G hue region boundary (rg)
Rg = 1 when 11 * R-8 * G-3 * B> 0, yg = 0 when they do not match
(3) GC hue region boundary (gc)
When 1 * R-5 * G + 4 * B <0, gc = 1, and when they do not match, gc = 0
(4) CB hue region boundary (cb)
Cb = 1 when 8 * R-14 * G + 6 * B <0, cb = 0 when they do not match
(5) BM hue region boundary (bm)
Bm = 1 when 9 * R-2 * G-7 * B <0, bm = 0 when they do not match
(6) MR hue region boundary (mr)
When R + 5 * G-6 * B <0, mr = 1;

(7) W pixel determination (R <thw) && (g <thw) && (B <thw)
Let y = m = c = 0.
(8) Y pixel determination (ry == 1) && (yg == 0) && (RGB difference> thy)
It is assumed that y = 1 and m = c = 0.
(9) G pixel determination (rg == 1) && (gc == 0) && (RGB difference> thg)
Let c = y = 1 and m = 0.
(10) C pixel determination (gc == 1) && (cb == 0) && (RGB difference> thc)
Let c = 1 and m = y = 0.
(11) B pixel determination (cb == 1) && (bm == 0) && (RGB difference> thb)
Let c = m = 1 and y = 0.
(12) M pixel determination (bm == 1) && (mr == 0) && (RGB difference> thm)
Let m = 1 and y = c = 0.
(13) If R pixel determination (mr = 1) && (ry == 0) && (RGB difference> thr),
It is assumed that y = m = 1 and c = 0.
(14) Bk pixel determination When the above conditions (7) to (13) are not satisfied, y = m = c = 1.

Here, the priority of the above (7) to (14) is given priority to the smaller number.
Further, thw, thy, thm, thc, thr, thg, thb are threshold values determined before copying (processing). The RGB difference is a difference between the maximum value and the minimum value of the image data of RGB in one pixel.

  The output signal is 3 bits of c, m, and y. That is, 3 bits represent c, m, y, r, g, b, and bk. Here, the threshold value is changed for each hue when the chromatic range is different for each hue area and the threshold value is determined according to the hue area. Note that the hue division in this case is an example, and the present invention is not limited to this, and may be another formula.

  The outputs c, m, and y of the hue division unit 1601 are stored in five lines in the line memories 1602 to 1604, respectively, and input to the color pixel determination unit 1605. The data of c, m, and y for five lines are input to the count units 1701 and 1702 and the pattern matching unit 1703.

  The pattern matching unit 1703 has 5 × 5 pixels (c, m, y) determined by the hue division unit 1601, and all of c, m, y are 1 (c = m = y = 1), or all are 0. Pattern matching is performed for pixels (color pixels) other than (c = m = y = 0). That is, if it matches any of the following patterns, it is determined as a color pixel candidate 2.

(1) Pattern 1
ary [y + 1] [x] && ary [y] [x] && ary [y-1] [x]
(2) Pattern 2
ary [y] [x + 1] && ary [y] [x] && ary [y] [x-1]
(3) Pattern 3
ary [y + 1] [x + 1] && ary [y] [x] && ary [y-1] [x-1]
(4) Pattern 4
ary [y + 1] [x-1] && ary [y] [x] && ary [y-1] [x + 1]

  The center pixel in the above is x, y. An example of this pattern is shown in FIG. As described above, the pattern matching unit 1703 performs pattern matching so as not to pick up isolated points. Conversely, when detecting a small area color such as a halftone dot, the pixel (color) other than the central pixel being 1 (c = m = y = 1) or all being 0 (c = m = y = 0) It may be determined depending on whether it is a pixel).

  The counting unit 1702 counts the number of the determined pixels (c, m, y) within 5 × 5. At this time, if the difference between the maximum value and the minimum value of the counted c, m, and y is greater than or equal to thcnt and the counted minimum value of c, m, and y is less than thmin, the color pixel candidate 1 is determined. The thcnt and thmin are threshold values set before copying (processing).

  It is assumed that y, m, and c are expanded into planes, the number is counted for each plane in an N × N matrix, and the minimum value is black (Bk). This makes it possible to correct even if the reading of the black pixel is shifted. Furthermore, it is assumed that the difference between the maximum value and the minimum value is a chromatic pixel. As a result, pixels whose black pixels are misread are corrected and chromatic pixels are extracted. That is, when there is a certain chromatic pixel, it is determined as a chromatic pixel.

  Outputs from the pattern matching unit 1703 and the count unit 1702 are determined by a color pixel determination unit 1705 as to whether they are color pixels. That is, if the color pixel candidate 1 and the color pixel candidate 2, it is determined that the input data is a color pixel.

  Next, the output of the color pixel determination unit 1705 is input to the blocking unit 1804. This operation is performed in the same manner as the blocking unit 1801, and the following processing operates in units of 4 × 4 blocks. The data blocked by the blocking unit 1804 is input to the continuous counting unit 1805.

The continuous count unit 1805 checks the continuity of the color pixel blocks, and determines whether it is a color document or a monochrome document, for example, as follows.
OUT = (number of blocks in which the chromatic pixel region is continuous in the main scanning direction ≧ RACS1)
# (Number of blocks in which a line having a chromatic pixel region continues in the sub-scanning direction ≧ RACS1)
The RACS 1 is a threshold value determined before copying (processing). Note that the continuous counting unit 1805 is configured to reduce hardware. Here, since the continuity is simply checked, even if the judgment conditions for the main scanning and the sub scanning are different, there is no problem.

  The count unit 1701 counts the number of pixels (c, m, y) determined by the hue determination unit 1601 within 5 × 5. That is, if the difference between the counted maximum value and the minimum value of c, m, y is equal to or less than thcnt, and the counted minimum value of c, m, y is equal to or greater than thmin, a black pixel is determined.

  Note that thcnt and thmin are threshold values determined before copying (processing). Here, y, m, and c are expanded into planes, the number is counted for each plane in an N × N matrix, and the minimum value is assumed to be black. As a result, it is possible to correct even if the reading of the black pixel is shifted.

  The difference between the maximum value and the minimum value is assumed to be a chromatic pixel. As a result, pixels whose black pixels are misread are corrected and chromatic pixels are extracted. A chromatic pixel is a case where a certain chromatic pixel exists. Since pixels around the black pixel may be colored due to reading deviation, thmin is corrected.

  Also, the black pixel determination output from the black pixel determination unit 1704 is blocked by the blocking unit 1801. Blocking outputs a color pixel block if there are two or more color pixels in a 4 × 4 pixel matrix. In the processing after the blocking unit 1801, 4 × 4 is output as a block in units of blocks.

  Subsequently, in the isolated point removal unit 1802, if there is no black pixel block in the adjacent pixel of the target pixel, the isolated point is removed. When the color pixel block exists in the expansion unit 1803, the output of the isolated point removal unit 1802 is subjected to expansion processing on three block pixels. The reason why the expansion processing is performed is to perform black character processing around the black pixel. Here, the output B / C signal outputs H when the pixel block is black, and outputs L otherwise.

  By the way, in the count unit 1701 and the count unit 1702, an increase in the memory amount due to the determination condition of chromatic (color) pixels / achromatic (black) pixels is reduced. Originally, it is better to make it independent from the hue division unit 1601, but making the hue division independent is not preferable because it increases the memory for pattern matching.

  The above-described embodiment has been performed on RGB data. However, the present invention is not limited to RGB data. For example, it is easy to perform a hue determination on a luminance color difference (Lab color space or the like). .

1 is a block diagram illustrating a schematic configuration of an image processing apparatus according to an embodiment of the present invention. It is a block diagram which shows the internal structure of the image process part in FIG. FIG. 3 is a block diagram illustrating an internal configuration of a document recognition unit in FIG. 2. It is explanatory drawing which shows the symbolized matrix for demonstrating the operation | movement of the white area detection or black area detection in the Laplacian part of a line drawing recognition part. It is sectional drawing of a line drawing, and is a conceptual diagram which shows the relationship between a white area | region, a black area | region, and a threshold value. It is explanatory drawing which shows the relationship of edge amount (x). It is a flowchart of a pattern matching process. It is a flowchart of a pattern matching process (pattern matching part). It is explanatory drawing which shows the setting of the attention pixel in a pattern matching process. It is a flowchart which shows the pattern matching process (pattern matching part) which concerns on embodiment of this invention. It is explanatory drawing which shows propagation of the state variable MFB in the pattern matching process of FIG. It is explanatory drawing which shows the example of judgment of the thin line by the difference in the state variable in the pattern matching part of a line drawing recognition part. It is explanatory drawing which shows the example of the interpolation process in the expansion part of a line drawing recognition part. It is a flowchart which shows the pattern matching process (pattern matching part) which concerns on embodiment of this invention. It is explanatory drawing which shows the detail emphasis effect by unsharp masking. It is a block diagram which shows the structure of the color determination part which is the principal part which concerns on embodiment of this invention. It is a block diagram which shows the internal structure of the color pixel determination part in FIG. It is a block diagram which shows the internal structure of the color pixel determination part in FIG. It is explanatory drawing which shows the example of a pattern used for pattern matching.

Explanation of symbols

101 Document Reading Unit 102 Image Processing Unit 103 Image Recording Unit 201 RGBγ Correction Unit 202 Document Recognition Unit 203 Delay Unit 204 RGB Filter Unit 205 Color Correction Unit 206 UCR Unit 207 Scaling Unit 208 CMYBk Filter Unit 209 CMYBkγ Correction Unit 210 Tone Processing Unit 301 line drawing recognition unit 302 color determination unit 303 monochrome conversion unit 304 Laplacian unit 305A, 305B pattern matching unit 306A, 306B isolated point removal unit 307A, 307B pixel density conversion unit 308A, 308B page memory 309A, 309B selector 310A, 310B isolated block Removal unit 311A, 311B Expansion unit 1601 Hue division unit 1602-1604 Line memory 1605 Color pixel determination unit 1701, 1702 Count unit 1703 Pattern matching 1704 Black pixel determination unit 1705 Color pixel determination unit 1801, 1804 Blocking unit 1802 Isolated point removal unit 1803 Expansion unit 1805 Continuous count unit

Claims (6)

It is determined for each pixel whether the pixel of the input image input from the outside is C, M, Y, R, G, B, BK, or W, and C, M, Y, R, Hue division means for representing each pixel of G, B, BK, W by (c, m, y) having a 3-bit value ;
In an n × m matrix (n and m are integers of 1 or more), which is a predetermined area in the input image including the pixels, the number of 1 is counted for each (c, m, y), and the count If the minimum value of the obtained values is a black pixel and the maximum value−minimum value is equal to or less than the first predetermined value, the pixel to be processed in the n × m matrix is determined to be an achromatic pixel region. Means,
An image processing apparatus comprising: The image according to claim 1, wherein the achromatic pixel determination unit determines that the processing target pixel is an achromatic pixel region when the minimum value is equal to or greater than a second predetermined value. Processing equipment. The achromatic pixel determination hand stage, whether the target pixel is achromatic pixel regions, to claim 1 or 2, characterized in that determined according to the difference between the maximum value and the minimum value of the RGB data The image processing apparatus described. The achromatic pixel determination hand stage is to set each threshold for each hue, whether the target pixel is achromatic pixel region, according to claim 1, wherein the determining in accordance with the threshold value The image processing apparatus according to any one of the above. The said achromatic pixel determination means is capable of changing the first predetermined value and the second predetermined value when detecting a plurality of achromatic pixels. Image processing device. The achromatic pixel determining means detects a plurality of achromatic pixels, determines whether the input image is a color image or a black and white image, and determines whether or not to process a specific area of the input image as a black character. The image processing apparatus according to claim 5 , wherein:

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