JP3733155B2 - Image processing apparatus and method - Google Patents

Description

[0001]
[Industrial application fields]
The present invention relates to an image processing apparatus and method, and more particularly, to an image processing apparatus and method for performing image region division processing for character recognition processing in an image.
[0002]
[Prior art]
With the recent progress of image processing technology, an image processing apparatus such as a computer has increased to process an image in which characters, photographs, graphs and the like are mixed. As a processing procedure in this case, first, an image to be processed is read by an image input device such as a scanner and input to the image processing device. The image processing apparatus performs region division processing for extracting a black pixel region, optical character recognition processing (hereinafter referred to as OCR processing), and the like for the input image, performs document management, and obtains the obtained image or character. Was used. The area dividing process is a process of dividing and extracting black pixels in an image to separate areas such as characters / photos / graphs / tables in the image according to their respective characteristics and extracting each area information. is there. The OCR process is a character recognition process for converting a bitmap that forms a character image into a character code for a character region in an image.
[0003]
Next, image processing including characters in the above-described conventional image processing apparatus will be described with reference to the flowchart of FIG.
[0004]
First, in step S221, an image to be a document is input by an image processing apparatus such as a scanner. This input is performed as, for example, 8-bit multi-value image data. Next, in step S222, the deterioration of the input original image, the inclination of the original, and the like are corrected, including the removal of the electrical noise of the scanner that occurs during image input. Next, in step S223, the input image is subjected to simple binarization with a predetermined fixed threshold value to perform region division processing, and each region data is output to extract a character region. The simple binarization process at this time may be a binarization process corresponding to the density of the input image, for example. In step S224, character recognition is performed by performing desired image processing such as OCR processing or pseudo-halftone processing for each extracted character region. Finally, in step S225, the processing result of the recognized character or the like is displayed.
[0005]
As described above, in the conventional image processing apparatus, the input image is simply binarized with a fixed threshold value or simply binarized according to the density of the original image, and then the region division process is performed, thereby performing image segmentation in the image. The document area was extracted and character recognition processing was performed.
[0006]
[Problems to be solved by the invention]
However, the conventional image processing apparatus has the following problems.
[0007]
For example, consider a case where the background of a document image is white and characters are drawn in black or gray. In this case, that is, when three or more luminance level targets are mixed in the original image, and simple binarization with a fixed threshold is performed on such an image, the gray character region is set to 2 as “white”. An area to be binarized and an area binarized as “black” are generated. Since these two regions occur irregularly, gray characters are crushed or blurred, and a highly accurate binarization result cannot be obtained. Therefore, accurate area division processing cannot be performed.
[0008]
Further, even when performing simple binarization according to the density of the original image, for example, the background is not purely “white”, so that the background is binarized with “white” and black and gray characters as “black”. Or, the base and gray letters are binarized as “white” and black letters as “black”. Accordingly, since it is impossible to binarize gray characters with high accuracy, accurate area division processing cannot be performed.
[0009]
Furthermore, the gray character region cannot be divided so as to be distinguished from the black character region, in particular, as a gray character region.
[0010]
The present invention has been made to solve the above-described problems. Even when an image having three or more luminance level objects is processed, binarization is performed with high accuracy for each luminance hierarchy. It is an object of the present invention to provide an image processing apparatus and method capable of performing accurate area division processing.
[0011]
[Means for Solving the Problems]
In order to achieve the above-described object, the present invention has the following configuration.
[0012]
That is, An image processing apparatus according to an aspect of the present invention includes a histogram calculation unit that calculates a histogram of a first input image, and a statistic that indicates a bias in the distribution of the histogram within a predetermined range. Threshold setting means for setting a binarization threshold, and the threshold setting means 2 According to the threshold value First Binarize the input image 2 Binarization means for generating a value image; Generated by binarization means Area dividing means for dividing an area from a binary image; , Generating a second input image by deleting the divided area from the first input image, and using the generated second input image instead of the first input image, the histogram calculation means and the Repetitive means for obtaining regions divided by luminance hierarchy by repeating processing by the threshold setting means and the region dividing means; It is characterized by having.
[0016]
[Action]
With the above configuration, a binarization threshold value is calculated for each luminance layer, a binary image for each luminance layer is created using the binarization threshold value for each luminance layer, and an area is divided from the binary image for each luminance layer. be able to. Therefore, it is possible to provide an image processing apparatus and method capable of performing an area division process for each luminance hierarchy in an image and a method for the same.
[0017]
【Example】
Hereinafter, an embodiment according to the present invention will be described in detail with reference to the drawings.
[0018]
FIG. 1 is a block diagram showing a system configuration including an image processing apparatus in this embodiment. Details of the operation in each configuration shown in FIG. 1 will be described later.
[0019]
In FIG. 1, 1 is an image processing apparatus that performs document management in this embodiment, 2 is an image input apparatus such as a scanner that inputs an image, and 3 is an image display apparatus that displays the processed image.
[0020]
In the image processing apparatus 1, 4 is an input unit serving as an interface with the image input apparatus 2, 5 is a storage unit such as a memory for storing data being processed, and 6 is a luminance frequency for accumulating luminance frequencies (histograms) of input images. Cumulative part. Reference numeral 7 denotes a binarization threshold value calculation unit that calculates a binarization threshold value for each luminance layer of the input image. Reference numeral 8 denotes a binary image for each luminance layer using the threshold value calculated by the binarization threshold value calculation unit 7. This is a binarization unit to be created. 9 is an area dividing unit that divides an image into areas for each attribute, 10 is a character recognition unit that performs character recognition processing on an area extracted as a character area by area division, and 11 is an area that is divided other than the character area. An image processing unit 12 that performs various types of image processing, and an output unit 12 that serves as an interface with the image display device 3. Each of these components is centrally controlled by a CPU (not shown).
[0021]
Hereinafter, the image processing in the image processing apparatus 1 will be described with reference to the flowchart of FIG.
[0022]
First, in step S <b> 21, an image to be a document is input to the image processing apparatus 1 via the input unit 4 by the image input apparatus 2. This input is performed as 8-bit multivalued image data, and the input image is stored in a working memory (not shown). In step S22, correction of the input image is performed, such as removal of electrical noise of the image input apparatus 2 generated during image input, degradation of the document image, document tilt, and the like. Next, proceeding to step S23, the binarization processing for each luminance layer by the luminance frequency accumulating unit 6, the binarization threshold calculation unit 7, and the binarization unit 8 and the region division processing by the region dividing unit 9 are performed. Details of the luminance layer-specific region division processing in step S23 will be described later.
[0023]
In step S24, the character recognition unit 10 performs OCR processing on the character region and the image processing unit 11 performs error diffusion on the character region other than the character region in accordance with the region division result for each luminance layer in step S23. Image processing such as pseudo-halftone processing such as method or dithering is performed. Then, the process proceeds to step S25, where each region subjected to image processing for each luminance layer is combined into one image, and the combined image is displayed on the image display device 3 via the output unit 12 in step S26.
[0024]
<Brightness layer-specific region division processing>
Next, the luminance layer-specific region dividing process shown in step S23 of FIG. 2 will be described in detail.
[0025]
In the present embodiment, a case where an image in which black characters and gray characters are drawn on a white background is processed will be described as an example.
[0026]
FIG. 4 shows an example of the luminance frequency (histogram) of an image including a character area processed in this embodiment.
[0027]
In the histogram shown in FIG. 4, the horizontal axis is a luminance value representing “0” on the left end and “255” on the right end, and this value is a digital value. That is, the left end of the horizontal axis corresponds to “black” and the right end corresponds to “white”. The vertical axis represents the frequency of each digital value. Therefore, in FIG. 4, the white background portion of the image to be processed has high luminance and therefore corresponds to the right mountain, the black character portion has low luminance and corresponds to the left mountain, and the gray character has medium luminance. Because there is, it corresponds to the central mountain.
[0028]
Hereinafter, the luminance layer-specific region division processing in this embodiment will be described with reference to the flowchart of FIG.
[0029]
First, in step S31, the luminance layer number is set to “1”. Here, the luminance hierarchy number is set based on the characteristic (number of peaks) of the luminance frequency in the image. In the example shown in FIG. 4, the luminance hierarchy number of black characters is “1”, The gray color luminance layer number is “2”, and the white luminance layer number is “3”. The luminance hierarchy number is assigned for each luminance level at which character recognition is finally desired.
[0030]
Subsequently, in step S32, simple binarization with a threshold most suitable for region division is performed as preprocessing for region division. Thereafter, in step S33, a black pixel region is extracted from the binarized image, divided into a character region, a photographic region, a graphic region, and the like based on the characteristics and the like, and region data described later is output. Then, the process proceeds to step S34, where area errors are removed as post-processing of area division. Details of the region error removal processing in step S34 will be described later. Next, in step S35, it is determined whether or not the luminance hierarchy number is “1” or less. If the luminance hierarchy number is “1” or less, that is, “1”, the process proceeds to step S36, and if it is not “1” or less, that is, “2” or more, the process ends.
[0031]
In step S35, it is determined whether or not the luminance layer number is “1” or less. This value is set by subtracting the fixed value “2” from the maximum value “3” of the luminance layer number. As a result, this embodiment can cope with an increase or decrease in the number of luminance layers.
[0032]
In step S <b> 36, the luminance pixel number is “1”, that is, the black pixel area divided as a black character is deleted from the input image as a divided area and stored in the storage unit 5. By performing this deletion, attention is paid only to the area excluding the black character area having the luminance layer number “1” when the region having the luminance layer number “2”, that is, the gray character region is divided. Can be processed. In step S37, “1” is added to the luminance hierarchy number, and the process returns to the region division pre-processing in step S32.
[0033]
As described above, in the luminance layer-specific region division processing according to the present embodiment, binarization is first performed when the luminance layer number is “1”, that is, a black character region, and region errors are removed after region division. After the divided black pixel region is deleted, binarization is performed for the case where the luminance layer number is “2”, that is, a gray character region, and region division and region error removal are performed.
[0034]
<< Regional region pre-processing (binarization processing) >>
Next, the binarization process as the area division pre-process in step S32 of FIG. 3 will be described in detail with reference to the flowchart of FIG. The area division pre-processing in step S32 is executed twice, when the luminance layer number is “1” and when it is “2”. Therefore, TH1 and TH2 are used as parameters as the binarization threshold values corresponding to each, but in FIG. 5, these are collectively expressed as TH.
[0035]
First, a case where the luminance hierarchy number is “1” will be described.
[0036]
In step S51 in FIG. 5, a histogram of the input image is calculated. Here, using all the pixels in the image, the frequency for each digital value from 8 bits, that is, “0” to “255” is calculated. Thereby, for example, the histogram shown in FIG. 4 is obtained.
[0037]
In step S52, "0" and "255" are set in the parameters START and END, respectively. START and END correspond to the start point and end point of the statistical value of the brightness value obtained in the subsequent steps S53 and S54, respectively.
[0038]
In step S53, the average value AV of the pixels corresponding to the digital values from START to END is calculated. For example, if START = 0 and END = 255, an average value AV of pixels having values of “0” to “255” (in this case, all pixels) is calculated. If START = 0 and END = 177, “ An average value AV of pixels having values from “0” to “177” is calculated.
[0039]
In step S54, the skew value SK of the pixel corresponding to the luminance value from START to END is calculated. The skew value is a statistic indicating the bias of the histogram distribution. For calculating the skew value, the following equation (1) is used.
[0040]
SK = (Σ (Xi−AV) ^ 3) / D (1)
(Note that R ^ 3 indicates the third power of R.)
Here, Xi is the luminance value of the pixel. D is a dispersion value of the entire image, and is calculated by the equation (2).
[0041]
D = Σ (Xi-AV) ^ 2 (2)
(Note that R ^ 2 indicates the square of R.)
Subsequently, in steps S55 and S56, the direction of bias of the histogram is determined. First, in step S55, the direction of the histogram bias is determined by the following equation (3). This is a determination as to whether or not the histogram bias is in the left direction.
[0042]
SK <−1.0 (3)
If the expression (3) is true in step S55, the process proceeds to step S59, and if false, the process proceeds to step S56. In step S59, START is not changed, and average value AV is set to END. Then, returning to step S53, the average value AV from the START value to the END value is calculated again.
[0043]
On the other hand, in step S56, the bias direction of the histogram is determined by the following equation (4). This is a determination as to whether or not the histogram bias is in the right direction.
[0044]
SK> 1.0 (4)
If the expression (4) is true in step S56, the process proceeds to step S510, and if false, the process proceeds to step S57. In step S510, the average value AV is set to START, and END is not changed. Then, returning to step S53, the average value AV from the START value to the END value is calculated again.
[0045]
On the other hand, in step S57, the average value AV when the conditions in steps S55 and S56 are both false is set as the binarization threshold TH1. In step S58, a simple binarization process using the binarization threshold TH1 is performed.
[0046]
As described above, in this embodiment, the binarization process when the luminance hierarchy number is “1” is performed. By this process, an appropriate binarization threshold is set for the area in which black characters are drawn, and a simple binarization process is performed. In other words, appropriate binarization is performed in the black character region.
[0047]
Next, using the example of the histogram shown in FIG. 4, the above-described determination process of the binarization threshold TH1 will be described in more detail.
[0048]
FIG. 6 is a diagram showing changes in the values of parameters when the above-described region division preprocessing shown in FIG. 5 is performed on an image having a histogram as shown in FIG. Each parameter value shown in FIG. 6 is indicated by the number of times of passing through steps S53 and S54 in the flowchart of FIG.
[0049]
First, in the first process that passes through steps S53 and S54, the average value AV and the statistic SK are calculated with START = 0 and END = 255, and values of “185” and “−102.5” are obtained, respectively. In this case, since the statistic SK is less than “−1.0”, START = 0 and END = 185 are set in step S59.
[0050]
Subsequently, in the second processing, the average value AV and the statistic SK at START = 0 and END = 185 are calculated, and values of “98” and “−14.6” are obtained, respectively. Again, since the statistic SK is less than “−1.0”, START = 0 and END = 98 are set in step S59.
[0051]
Subsequently, in the third processing, the average value AV and the statistic SK at START = 0 and END = 98 are calculated, and values of “45” and “10.7” are obtained, respectively. In this case, since the statistic SK exceeds “1.0”, START = 45 and END = 98 are set in step S510.
[0052]
Subsequently, in the fourth processing, the average value AV and the statistic SK at START = 45 and END = 98 are calculated, and values of “77” and “−8.2” are obtained, respectively. Again, since the statistic SK is less than “−1.0”, START = 45 and END = 77 are set in step S59.
[0053]
Subsequently, in the fifth processing, the average value AV and the statistic SK at START = 45 and END = 77 are calculated, and the values “63” and “−3.0” are obtained, respectively. Again, since the statistical quantity SK is less than “−1.0”, START = 45 and END = 63 are set in step S59.
[0054]
In the sixth processing, the average value AV and the statistical quantity SK at START = 45 and END = 63 are calculated, and values of “54” and “−0.9” are obtained, respectively. Here, since the statistic SK is not less than “−1.0” and not more than “1.0”, the condition of Steps S55 and S56 is not satisfied, and the process proceeds to Step S57 to set “54” as the binarization threshold TH1. Is set. In step S58, a simple binarization process using the binarization threshold TH1 is performed.
[0055]
As described above, the binarization threshold is determined so that the skew value converges to a predetermined value.
[0056]
The binarization process when the luminance hierarchy number is “1” has been described above. Next, the above-described flowchart of FIG. 5 illustrates the binarization process for medium luminance characters whose luminance hierarchy number is “2”. The same is described with reference to FIG.
[0057]
First, in step S51, a histogram of the target image is calculated. In this case, the divided area (black character area) having the luminance layer number “1” is deleted in step S36 in FIG. 3 described above, and the luminance layer number is set to “2” in step S37. In step S51, the frequency with respect to the luminance value corresponding to the luminance layer number “2” can be calculated using pixels other than the divided region whose luminance layer number is “1” in the image.
[0058]
As a result, the histogram obtained in step S51 has a shape as shown in FIG. In FIG. 4 and FIG. 7, the scale of the vertical axis is different. The processing from step S52 to step S58 is the same as that in the case where the above-described luminance hierarchy number is “1”. Of course, the binarization threshold TH2 determined is different from the binarization threshold TH1 described above. ing.
[0059]
Hereinafter, the binarization threshold value TH2 determination process described above will be described in more detail using the example of the histogram shown in FIG.
[0060]
FIG. 8 is a diagram showing changes in the values of the parameters when the above-described region division preprocessing shown in FIG. 5 is performed on an image having a histogram as shown in FIG. Each parameter value shown in FIG. 8 is indicated by the number of times of passing through steps S53 and S54 in the flowchart of FIG.
[0061]
First, in the first process that passes through steps S53 and S54, the average value AV and the statistic SK are calculated with START = 0 and END = 255, and values of “186” and “−53.4” are obtained, respectively. In this case, since the statistic SK is less than “−1.0”, START = 0 and END = 186 are set in step S59.
[0062]
Subsequently, in the second process, the average value AV and the statistic SK at START = 0 and END = 186 are calculated, and values of “122” and “4.2” are obtained, respectively. In this case, since the statistic SK exceeds “1.0”, START = 122 and END = 186 are set in step S510.
[0063]
Subsequently, in the third processing, the average value AV and the statistic SK at START = 122 and END = 186 are calculated, and values of “149” and “5.8” are obtained, respectively. In this case, since the statistic SK exceeds “1.0”, START = 149 and END = 186 are set in step S510.
[0064]
In the fourth process, the average value AV and the statistical quantity SK at START = 149 and END = 186 are calculated, and values of “167” and “−0.5” are obtained, respectively. Here, since the statistic SK is not less than “−1.0” and not more than “1.0”, the condition shown in Steps S55 and S56 is not satisfied, so the process proceeds to Step S57 and the binarization threshold TH2 = 167. Is set. In step S58, simple binarization processing is performed using the binarization threshold TH2.
[0065]
As described above, the binarization process is performed on the area having the luminance hierarchy number “2”.
[0066]
As described above, the binarization threshold TH1 when the luminance hierarchy number is “1” and the binarization threshold TH2 when the luminance hierarchy number is “2” are in a relationship as shown in FIG. Become. The histogram shown in FIG. 9 is the same as that of FIG. 4, and represents the luminance frequency of all pixels. That is, first, the luminance hierarchy number “1” is binarized with the binarization threshold TH1 = 54, and the region A that becomes “black” is divided from the region B that becomes “white”. For the luminance layer number “2”, the region A is deleted from all pixels, that is, the image of the region B is binarized with the binarization threshold TH2 = 167, and the region C that becomes “black” is “white”. Is divided from the region D which becomes “.
[0067]
As described above, according to FIG. 9, the luminance values of all the pixels in this embodiment are “0 to 54” (region A), “54 to 167” (region C), and “167 to 255” (region D). It can be seen that it is divided into three regions.
[0068]
<< area division processing >>
Next, the region dividing process shown in step S33 of FIG. 3 will be described in detail with reference to the flowchart of FIG.
[0069]
First, in step S101, a binary image input via the input unit 4 is input to the work memory. In step S102, the input image is thinned out so that m × n pixels become one pixel, and an image for region division is generated. At this time, if at least one black pixel exists in the m × n pixel, the m × n pixel is set as one black pixel.
[0070]
In step S103, for all the pixels of the region dividing image, region division is performed with a predetermined number of regions where black pixels are continuous in the up, down, left, and right directions as one region. At that time, each area is labeled by assigning a number in the order of detection of the area. Next, in step S104, the regions are classified by the width, height, area, and black pixel density in each region, and attributes are labeled. The area attributes include “table”, “outer frame area”, “text”, etc., as will be described in detail later.
[0071]
In step S105, the average of the width and height of all areas labeled “text” is calculated. If the obtained average width is larger than the average height, the processed image is regarded as a horizontally written document. In the opposite case, the character set is determined by regarding the document as vertically written. At the same time, the character size is set to one character with the average height for horizontal writing and the average width for vertical writing.
[0072]
In addition, the text column and line spacing are detected from the histograms of all the “text” areas in the vertical direction (horizontal writing) or horizontal direction (vertical writing) on the image for region division. In step S106, an area having a large character size in the “text” area is set to “title”. In step S107, the “title” area and the “text” area that remain apart without any relation are merged according to the distance from the surrounding areas to form a single area. Next, in step S108, region data such as attributes and coordinates and size in the original image are output to the storage unit 5 and stored for each region.
[0073]
By performing the above processing, in this embodiment, the region division processing of the binary image is performed, and each region data is obtained.
[0074]
FIG. 11 shows an example of the area data described above. Each area data item shown in FIG. 11 will be described below.
“No.”: Indicates the detection order of areas.
“Attribute”: indicates attribute information of the area, and the following eight types are prepared.
[0075]
“Root” Indicates the input image itself.
[0076]
“Text” Indicates a character area.
[0077]
“Title” Indicates a heading area.
[0078]
“Table” Indicates a table area.
[0079]
“Noise” Indicates an area that cannot be determined as a character area or an image area.
[0080]
“Outer frame” Indicates an area such as a ruled line.
[0081]
“Photo image” Indicates a photo area.
[0082]
“Line image” Indicates a line image area.
“Start point coordinates”: X and Y coordinates at the start of the area in the original image.
“End point coordinates”: X and Y coordinates at the end of the region in the original image.
“Number of pixels”: indicates the total number of pixels in the region.
"Character set information": Indicates three types of character set information: vertical writing, horizontal writing, and unknown.
[0083]
In the area data shown in FIG. 11, only the area where “attribute” is indicated by “text” holds the area data (row area data) related to the line before merging in step S107 shown in FIG.
[0084]
As described above, the region division processing is performed in this embodiment. The area data shown in FIG. 11 is merely an example to which the present embodiment is applied, and other information may be added or reduced as appropriate depending on the image processing apparatus.
[0085]
<< Process after region division (region removal processing) >>
Next, the post-region division processing shown in step S34 of FIG. 3 will be described in detail with reference to the flowchart of FIG. The post-region division processing in the present embodiment is processing for removing a region having a region division error among the regions divided into regions, and is hereinafter referred to as layout noise reduction (LNR).
[0086]
First, in step S121, it is determined whether each area data after the area division is a root area. The root area is an area surrounding the entire image, that is, the entire area. If it is the root area, the process proceeds to step S126, and the LNR process is not performed. If it is not the root area, the process advances to step S122 to determine whether the area is a text area (character area) or a noise area. If it is a text area or a noise area, the process proceeds to step S123, and if not, the process proceeds to step S125.
[0087]
In step S123, LNR processing 1 is performed in which the area data is removed as an area division error according to the area size, and in step S124, the area data is removed as an area division error according to the black ratio in the area. LNR processing 3 is performed. On the other hand, in step S125, LNR processing 2 is performed in which region data that is not a text region and not a noise region is removed as a region division error according to the region size. The LNR processes 1, 3, and 2 in steps S123, S124, and S125 will be described in detail below.
[0088]
In step S126, it is determined whether or not the processing for all the regions has been completed. If not completed, the process returns to step S121, and if completed, the LNR process is terminated.
[0089]
As described above, in this embodiment, a region that causes a region division error is removed from region data divided as a black pixel region by region division.
[0090]
Hereinafter, the LNR process 1 shown in step S123 will be described in detail. FIG. 13 is a flowchart showing the LNR process 1. First, in step S131, the height H1 and the width W1 are referred to from the region data of the processing target region. Then, in order to calculate the height threshold value HT1 and the width threshold value WT1 used for determining the size of the region, in step S132, the reading resolution SR of the image input device 2 such as a scanner and the minimum character that is not removed in the image Are set as MP1h and MP1w for the height and width, respectively.
[0091]
In this embodiment, the threshold values HT1 and WT1 are calculated by the following equations (5) and (6).
[0092]
HT1 = (SR / 72.0) × MP1h (5)
WT1 = (SR / 72.0) × MP1w (6)
In step S133, the height threshold value HT1 is calculated using equation (5). For example, when the resolution SR of the image input apparatus 2 is 400 dpi and the height point number MP1h of the minimum character in the image is 4 points, the height threshold value HT1 is calculated as “22”. In step S134, the height H1 of the area data is compared with the height threshold value HT1 calculated in step S133. If the height H1 of the area data is larger than the threshold value HT1, the process proceeds to step S135, and if smaller than the threshold value HT1, the process proceeds to step S138.
[0093]
In step S135, the width threshold value WT1 is calculated using equation (6). In step S136, the width W1 of the area data is compared with the threshold value WT1 of the width calculated in step S135. If the width W1 of the area data is larger than the threshold value WT1, the LNR process 1 ends. On the other hand, if the width W1 of the area data is smaller than the threshold value WT1, the process proceeds to step S137, and the ratio H1 / W1 between the height H1 and the width W1 of the area data is determined. When this ratio is “2” or less, the LNR process 1 ends. On the other hand, if the ratio exceeds “2”, it is determined that the area being processed is an area division error, and the process proceeds to step S138, where the area is removed.
[0094]
Next, the LNR process 3 shown in step S124 of FIG. 12 will be described in detail with reference to the flowchart of FIG. First, in step S141, the number of black pixels BC in the area is accumulated. In step S142, the black ratio BR1 in the area is calculated by the following equation (7).
[0095]
BR1 = BC / (W1 × H1) × 100 (7)
In step S143, a minimum black ratio BRT1 and a maximum black ratio BRT2 are set. BRT1 and BRT2 are set in advance according to the black ratio characteristic of the character, for example, BRT1 = 5 and BRT2 = 52.
[0096]
In step S144, the black ratio BR1 in the region is compared with the minimum black ratio BRT1 and the maximum black ratio BRT2. If the black ratio BR1 is smaller than the minimum black ratio BRT1 or larger than the maximum black ratio BRT2, it is determined that the area being processed is an area division error, and the process proceeds to step S145 to remove the area. In other cases, the LNR process 3 ends.
[0097]
Next, the LNR process 2 shown in step S125 of FIG. 12 will be described in detail with reference to the flowchart of FIG. First, in step S151, the height H2 and the width W2 are referred to from the region data of the processing target region. In step S152, the resolution SR of the image input device 2 is set in order to calculate the height threshold value HT2 and the width threshold value WT2 used to determine the size of the region. In step S153, it is determined whether or not the attribute of the area being processed is an outer frame area. If it is an outer frame area, the process proceeds to step S154. If it is not an outer frame area, the process proceeds to step S157.
[0098]
In step S154, the minimum number of points MP21 is set as MP21h and MP21w for the height and width, respectively. Similarly, in step S157, the minimum number of points MP22 is set as MP22h and MP22w for the height and width, respectively. Here, the minimum number of points MP21 and MP22 represents the minimum size of the area not to be removed by the number of character points depending on whether or not it is an outer frame area in the LNR process 2.
[0099]
In steps S155 and S158, height threshold values HT21 and HT22 and width threshold values WT21 and WT22 are calculated by the above-described equations (5) and (6). For example, when the resolution SR of the image input apparatus 2 is 400 dpi and the minimum number of points MP22 is 4 points in both height and width, the thresholds HT22 and WT22 are calculated as “22”. In steps S156 and S159, a height threshold HT2 and a width threshold WT2 are set, respectively.
[0100]
In step S1510, the height H2 of the area data is compared with the threshold value HT2 of the height set in steps S156 and S159, and the width threshold value WT2 is compared with the width W2 of the area data. If the height H2 of the area data is smaller than the threshold value HT2 or the width W2 is smaller than the threshold value WT2, it is determined that the area being processed is an area division error, and the area is removed in step S1511. In other cases, the LNR process 2 is terminated.
[0101]
As described above, the LNR process of this embodiment removes an area determined to be an area division error by three types of processes.
[0102]
<Image processing by brightness layer>
Next, the luminance layer-specific image processing shown in step S24 of FIG. 2 will be described in detail with reference to the flowchart of FIG.
[0103]
First, in step S161, the area data which is deleted from the input image in step S36 of FIG. 3 and stored in the storage unit 5 and whose luminance hierarchy number is “1” is read. In step S162, it is determined whether or not the character area is based on the attribute of the area data. If it is a character area, the process proceeds to step S163, and the simple binarization is performed with a threshold most suitable for document recognition processing by OCR in the subsequent stage, as will be described in detail later. In step S164, OCR processing is performed. On the other hand, if it is not a character area, it is regarded as an image area, and the process proceeds to step S165 to perform binarization by performing pseudo halftone processing such as an error diffusion method or a dither method.
[0104]
Next, in step S166, it is determined whether an unprocessed area remains. If it remains, the process proceeds to step S162, and if not, the process proceeds to step S167. In step S167, it is determined whether or not the luminance layer number of the area data being processed is “1”. If “1”, the process proceeds to step S168. To do. In step S168, the area data whose luminance hierarchy number is “2” is read, and the process returns to step S162.
[0105]
That is, in the present embodiment, character recognition processing is performed independently for each character region for each luminance layer.
[0106]
As described above, according to the present embodiment, even when processing an image in which three or more luminance levels (white, gray, black) are mixed, high-precision binarization processing is performed even for gray characters. Therefore, accurate area division processing can be performed over a plurality of luminance hierarchies.
[0107]
[Other embodiments]
In the above-described embodiment, the description has been given assuming that there are three luminance layers. However, the present invention is not limited to this example, and the number of luminance layers may be determined according to an actual image.
[0108]
In the embodiment, the input image is 8-bit multi-value image data. However, the present invention is not limited to this. For example, a color image or the like, that is, multi-bit information is used as image information for binarization. I just need it.
[0109]
Further, the sampling in the image when calculating the histogram may be all pixels or every few pixels, and is not limited. Further, the calculation of the average AV, the statistic SK, and the like is not necessarily performed with 8 bits, and may be performed with a small number of bits in order to increase the speed and reduce the memory.
[0110]
Further, although the convergence condition of the skew value SK, which is a statistic, is set to ± 1.0, it is not limited to this. What is necessary is just to be comprised so that the threshold value of binarization may be determined using skew value SK.
[0111]
Further, by providing luminance hierarchy information in the area data, it is possible to perform character recognition by distinguishing between the gray character area and the black character area.
[0112]
Note that the present invention may be applied to a system composed of a plurality of devices such as an image scanner, a printer controller, and a printer, or to an apparatus composed of a single device such as a color copying machine. Further, the present invention is not limited to the hardware provided in the image processing apparatus as described above, and can be applied to a case where the image processing apparatus is achieved by supplying a program stored in a medium such as a magnetic disk to the system or apparatus. Needless to say.
[0113]
【The invention's effect】
As described above, according to the present invention, binarization threshold values are calculated for each luminance layer, binarization is performed, and then region division is performed for each luminance layer, thereby mixing three or more luminance level targets. Even when an image is processed, a highly accurate binarization result can be obtained for each luminance level, so that accurate region division processing can be performed across a plurality of luminance layers.
[0114]
Furthermore, since each region can be distinguished for each of a plurality of luminance layers, it is possible to perform region division processing by distinguishing a gray character region from a black character region.
[0115]
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a system configuration of an image processing apparatus according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating image processing including characters according to the present exemplary embodiment.
FIG. 3 is a flowchart showing a luminance layer-specific region dividing process in the present embodiment.
FIG. 4 is a diagram illustrating an example of a histogram of a document image in the present embodiment.
FIG. 5 is a flowchart showing region division pre-processing in the present embodiment.
FIG. 6 is a diagram illustrating an example of each variable value in the region division pre-processing according to the present embodiment.
FIG. 7 is a diagram illustrating an example of a histogram of an area where the luminance hierarchy number of the document image is “2” in the present embodiment.
FIG. 8 is a diagram illustrating an example of each variable value in the region division pre-processing according to the present embodiment.
FIG. 9 is a diagram illustrating a binarization result for each luminance layer in the region division pre-processing in the present embodiment.
FIG. 10 is a flowchart showing area division processing in the present embodiment.
FIG. 11 is a diagram illustrating an example of region data obtained by region division processing in the present embodiment.
FIG. 12 is a flowchart illustrating post-region division processing according to the present exemplary embodiment.
FIG. 13 is a flowchart showing region removal processing 1 according to the size of a region in the present embodiment.
FIG. 14 is a flowchart showing region removal processing 2 based on black ratio in the present embodiment.
FIG. 15 is a flowchart showing region removal processing 3 based on the size of a region in the present embodiment.
FIG. 16 is a flowchart showing image processing by brightness hierarchy in the present embodiment.
FIG. 17 is a flowchart showing image processing including characters in a conventional image processing apparatus.
[Explanation of symbols]
1 Image processing device
2 Image input device
3 Image display device
4 Input section
5 storage unit
6 Luminance frequency accumulation section
7 Binarization threshold value calculation part
8 Binarization part
9 Area division part
10 Character recognition part
11 Image processing unit
12 Output section

Claims (10)

Histogram calculating means for calculating a histogram of the first input image;
Threshold setting means for setting a binarization threshold so that a statistic indicating the bias of the histogram distribution falls within a predetermined range ;
And binarizing means for generating a binary image by binarizing the first input image by the binarization threshold value set by the threshold value setting means,
Area dividing means for dividing an area from the binary image generated by the binarizing means ;
A second input image is generated by deleting the divided area from the first input image, and the generated second input image is used instead of the first input image, and the histogram calculation means and the threshold value are used. Repetitive means for acquiring regions divided by luminance hierarchy by repeating the processing by the setting means and the region dividing means;
An image processing apparatus comprising: Before SL threshold value setting means, between the start of the statistic calculation (START) and end point (END), and calculates the average value of the histogram (AV), the calculated average value (AV) brightness of each pixel And calculating a statistic indicating the histogram distribution bias based on the value, determining whether the calculated statistic falls within the predetermined range, and determining that the statistic falls within the predetermined range, the average value (AV) Is set as the binarization threshold value. On the other hand, if it is determined that the threshold value does not fit, the statistical value calculation start point (START) and end point (END) are reset using the average value (AV). the image processing apparatus according to claim 1, wherein repeating the amount of computation. The image processing apparatus according to claim 2 , wherein an initial value of the start point (START) is set to 0 and an initial value of the end point (END) is set to 255 . The image processing apparatus according to claim 1, wherein the binarization unit generates the binary image by simple binarization using the set binarization threshold . Of the areas divided by the area dividing means, further comprising a removing means for removing an area that has been erroneously divided,
The repeating means generates a second input image by deleting the divided area other than the area removed by the removing means from the first input image, and generates the generated second input image as the second input image. The area divided according to the luminance hierarchy is obtained by repeating the processing by the histogram calculating unit, the threshold setting unit, and the region dividing unit using the first input image instead of the first input image. The image processing apparatus described. A histogram calculation step of calculating a histogram of the first input image;
A threshold setting step for setting a binarization threshold so that a statistic indicating a bias in the distribution of the histogram falls within a predetermined range ;
And binarization to produce a binary image by binarizing the first input image by the binarization threshold value set by the threshold setting step,
A region dividing step of dividing a region from the binary image generated in the binarization step ;
A second input image is generated by deleting the divided area from the first input image, and the generated second input image is used instead of the first input image, and the histogram calculation step and the threshold value By repeating the process by the setting step and the region dividing step, a repeating step of acquiring the region divided by luminance hierarchy,
An image processing method comprising: Before SL threshold setting step, between the starting point of the statistic calculation (START) and end point (END), and calculates the average value of the histogram (AV), the calculated average value (AV) and the luminance of each pixel And calculating a statistic indicating the histogram distribution bias based on the value, determining whether the calculated statistic falls within the predetermined range, and determining that the statistic falls within the predetermined range, the average value (AV) Is set as the binarization threshold value. On the other hand, if it is determined that the threshold value does not fit, the statistical value calculation start point (START) and end point (END) are reset using the average value (AV). 7. The image processing method according to claim 6, wherein the amount calculation is repeated . 8. The image processing method according to claim 7 , wherein an initial value of the start point (START) is 0, and an initial value of the end point (END) is 255 . The image processing method according to claim 6 , wherein the binarization step generates the binary image by simple binarization using the set binarization threshold . Of the regions divided in the region dividing step, further comprising a removal step of removing a region that is erroneously divided,
In the repetition step, a second input image is generated by deleting the divided region other than the region removed in the removal step from the first input image, and the generated second input image is The area divided according to the luminance hierarchy is obtained by repeating the process of the histogram calculation step, the threshold setting step, and the region dividing step using the first input image instead of the first input image. The image processing method as described.

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