JP2006304063A - Image processing apparatus, image processing method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To accurately discriminate a character region from a non-character region with respect to an image processing apparatus, image processing method and a program. <P>SOLUTION: This method has steps of generating a binary document image from a multi-level document image, extracting a determination target region from the generated binary image, applying each of first and second line thinning processing to the image of the determination target region, calculating each of color dispersion value of the multilevel document image for the position of each of line thinning processing, and determining whether the determination target region consists of characters or photographs on the basis of the calculated first and second color dispersion values. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an image processing apparatus, an image processing method, and a program that can suitably determine a region of a document image.

  In recent years, with the widespread use of color printers, color scanners, etc., the number of colorized documents has increased, and there has been an increased opportunity to capture and store them as electronic files or send them to third parties via the Internet. Yes. However, if the full color data is used as it is, the load on the storage device and the line is large, so it is necessary to reduce the amount of data handled by a method such as compression processing.

  Conventionally, as a method for compressing a color image, for example, a method of compressing a binary image having a pseudo gradation by error diffusion or the like, a method of compressing in a JPEG format, and a ZIP compression by converting to an 8-bit palette color And a method of performing LZW compression. In addition, there is a compression method or the like in which high quality is obtained for a normal character region by combining region determination, binary compression by MMR, lossless compression by ZIP, and irreversible compression by JPEG (for example, Patent Document 1). And Patent Document 2).

  Conventionally, as a technique related to document image processing, there is an optical character recognition apparatus (OCR) technique that optically inputs a document, recognizes characters, and outputs a text code (see, for example, Patent Document 3). ).

In OCR, character lines are cut out (extracted) by density projection (histogram), and character blocks are cut out (extracted) in units of characters. When cutting out a character block, the density projection is taken in the direction of the character line, the character lines are separated based on the change in the density projection value, and each character line is taken separately by taking the density projection in the direction perpendicular to the character line. Extract a block of characters. Also, if necessary, a final character block that becomes a character image of one character is cut out using information such as a standard character size and character pitch estimated value, and a density projection value in a direction perpendicular to the line. . The extracted character block is subjected to normalization of vertical and horizontal dimensions and then subjected to predetermined feature data extraction processing. For each character block from which feature data is extracted, a similarity with a standard pattern obtained in advance is calculated, and a character with the highest similarity is taken as a recognition result. A set of standard patterns is called a recognition dictionary.
JP 2002-077633 A JP 2004-128880 A JP 2003-346083 A

  According to the methods described in Patent Document 1 and Patent Document 2, high quality is obtained for a normal character area by combining area determination, binary compression by MMR, lossless compression by ZIP, and lossy compression by JPEG. It is done. However, as a result of area determination, there is a case where an area that is not a character (photo area or the like, hereinafter, non-character) is erroneously determined to be a character, and in that case, there is a problem that a large image quality deterioration occurs.

  In the OCR process, if the area cut out as a character block is a non-character, character recognition is performed on the non-character. If character recognition is performed on non-characters, the overall processing speed is reduced, and there is a problem that meaningless text codes may be included in the output data of the recognition result, which is not preferable. .

  The present invention has been made in consideration of such circumstances, and an image processing apparatus, an image processing method, and a computer program capable of satisfactorily performing character and non-character attribute determination on an extracted region. The purpose is to provide.

  In order to solve the above problems, an image processing apparatus according to the present invention extracts binarization means for generating a binary document image from a multilevel document image, and a determination target region from the generated binary image. Area extraction means; first thinning means for thinning the image of the determination target area by a first thinning process; and second thinning for thinning the image of the determination target area by a second thinning process. Means, a first color dispersion value calculating means for calculating a first color dispersion value of the multi-value document image with respect to a position corresponding to a thinning result by the first thinning means, and a thin line by the second thinning means Based on the second color dispersion value calculating means for calculating the second color dispersion value of the multi-value document image with respect to the position corresponding to the conversion result, and the calculated first color dispersion value and second color dispersion value. Area determination means for determining whether the determination target area is a character or a photograph. And wherein the Rukoto.

  In order to solve the above problems, an image processing method according to the present invention includes a binarization step for generating a binary document image from a multi-level document image, and extracting a determination target region from the generated binary image. An area extraction step; a first thinning step for thinning the image of the determination target area by a first thinning process; and a second thinning for thinning the image of the determination target area by a second thinning process. A first color dispersion value calculating step for calculating a first color dispersion value of the multi-value document image with respect to a position corresponding to a thinning result obtained by the first thinning step, and a thin line obtained by the second thinning step. A second color dispersion value calculating step of calculating a second color dispersion value of the multi-value document image with respect to a position corresponding to the conversion result, and the calculated first color dispersion value and second color dispersion value. And whether the judgment target area is a character And having a determining area determination step of determining.

  According to the present invention, it is possible to accurately perform the character / non-character region determination. Therefore, when this region determination result is applied to a compression technique, good image quality can be obtained and the compression efficiency can be improved. Moreover, when applied to the OCR technology, it is possible to improve the recognition rate by suppressing the output of meaningless text codes as well as the processing speed.

Example 1
In the following embodiment, an example will be described in which the region determination technique of the present invention is applied to a color image compression technique that can be mounted on, for example, a color copying machine. The color copying machine functions include, for example, a color copy function, a color print function, and a color scanner function. The area determination technology described in the present embodiment is applicable to the color copy function and the color scanner function. It is. Specifically, the present invention can be applied to a compression technique used when compressing color image data obtained by reading a color original. As the color scanner function, for example, a data transmission function for compressing color image data obtained by reading a color original and transmitting the compressed data to the outside, and the color image data are compressed and stored in a storage unit inside the copier. There is a save function to do.

  Hereinafter, the present invention will be described in detail according to preferred embodiments with reference to the drawings.

  FIG. 1 is a schematic diagram showing a system configuration according to an embodiment of the present invention. A multifunction peripheral (MFP) 101 having a network communication function and a host computer (hereinafter referred to as PC) 102 are connected by a transmission medium such as a network 103. FIG.

  Dotted lines 104 to 105 indicate the flow of processing / control, and will be described in the following order. Reference numeral 104 denotes a process in which the user reads a paper document from the scanner of the MFP 101. At that time, the user can send a destination (for example, PC 102), various transmission settings (for example, resolution, compression rate), data format (for example, JPEG, TIFF, etc.) from the user interface (203 in FIG. 2) described later. PDF, PDF high compression, and PDF (with OCR result)) are designated in advance. In this embodiment, in order to explain an example of using the region determination method of the present invention in a color image compression technique, a case where PDF high compression is designated as a data format will be described. The technical details of PDF high compression will be described later. Reference numeral 105 denotes processing for generating data using a software or hardware function described later of the MFP 101 based on the specified transmission setting and data format, and transmitting the data to the specified destination. Here, since the image transmitted to the PC 102 is transmitted in a file format such as PDF, it can be viewed with a general-purpose viewer of the PC 102.

  Next, a detailed hardware configuration of the MFP 101 in FIG. 1 will be described with reference to FIG.

  The MFP 101 includes a scanner unit 201 that is an image input device, a printer unit 202 that is an image output device, a control unit 204 that includes a CPU and a memory, an operation unit 203 that is a user interface, and the like. The control unit 204 is connected to the scanner unit 201, the printer unit 202, and the operation unit 203. On the other hand, the control unit 204 is connected to a LAN 219 or a public line (WAN) 220, which is a general telephone line network, so that image information and device information can be stored. A controller that performs input and output. A CPU 205 is a controller that controls the entire system. A RAM 206 is a system work memory for the CPU 205 to operate, and is also an image memory for temporarily storing image data. A ROM 210 is a boot ROM, and stores a system boot program. An HDD 211 is a hard disk drive and stores system control software and image data. An operation unit I / F 207 is an interface unit with the operation unit (UI) 203 and outputs image data to be displayed on the operation unit 203 to the operation unit 203. Also, the CPU 205 serves to transmit information input by the user of the image processing apparatus from the operation unit 203 to the CPU 205. A network 208 connects the image processing apparatus to the LAN 219 and inputs / outputs packet format information. A modem (MODEM) 209 connects the image processing apparatus to the public line 220, and performs input / output by demodulating / modulating information. The above devices are arranged on the system bus 221.

  An image bus interface (Image Bus I / F) 212 is a bus bridge that connects a system bus 221 and an image bus 222 that transfers image data at high speed, and converts a data structure. The image bus 222 is composed of, for example, a PCI bus or IEEE1394.

  The following devices are arranged on the image bus 222. A raster image processor (RIP) 213 analyzes the PDL code and develops it into a bitmap image. The device I / F unit 214 connects the scanner unit 201 which is an image input / output device via the signal line 223 and the printer unit 202 via the signal line 224 to the control unit 204, respectively. Perform system conversion. A scanner image processing unit 215 corrects, processes, and edits input image data. The printer image processing unit 216 performs correction, resolution conversion, and the like according to the printer unit 202 with respect to print output image data to be output to the printer unit 202. The image rotation unit 217 rotates the input image data and outputs it. The image compression unit 218 performs JPEG compression / decompression processing on multi-valued image data or device-specific compression / decompression processing, and JBIG, MMR, and MH compression / decompression processing on binary image data. . The above is the detailed hardware configuration of the MFP 101 in FIG.

  Next, a software configuration installed in the control unit 204 in FIG. 2 will be described with reference to FIG. A user interface (hereinafter referred to as UI) 301 is a module that mediates between a device and a user operation when the operator performs various operations / settings on the MFP using the operation unit 203. In accordance with the operation of the operator, this module transfers input information to various modules to be described later, and requests processing or sets data.

  Reference numeral 302 denotes an address book (Address-Book), that is, a database module that manages a data transmission destination, a communication destination, and the like. The contents of the address book 302 are detected by the UI 301 when an operation from the operation unit 203 is detected, and data is added, deleted, and acquired, and data transmission / communication destination information is given to each module described later by the operator's operation. It is what is used.

  Reference numeral 303 denotes a Web server module (Web-Server module), which is used to notify management information of the MFP in response to a request from a Web client (for example, the PC 102). This management information includes an after-mentioned integrated transmission unit (Universal-Send module) 304, a later-described remote copy scan module (Remote-Copy-Scan module) 309, a later-described remote copy print module (Remote-Copy-Print module) 310, The data is read via a control API (Control-API) 318, which will be described later, and notified to the Web client via an HTTP module 312, a TCP / IP communication module 316, and a network driver (Network-Driver) 317, which will be described later. The Web server module 303 creates information to be passed to the Web client as data in a so-called Web page (homepage) format such as an HTML format. Java (registered trademark), a CGI program, or the like is used as necessary.

  Reference numeral 304 denotes an integrated transmission unit (Universal-Send module), that is, a module that manages data distribution, and distributes data designated by the operator via the UI 301 to an instructed communication (output) destination. When the operator instructs the generation of distribution data using the scanner function of the MFP, the scanner 201 of the MFP is operated via a control API 318 described later to generate data.

  A module 305 is executed when a printer is designated as an output destination in the integrated transmission unit 304. Reference numeral 306 denotes a module that is executed when an E-mail address is designated as a communication destination in the integrated transmission unit 304. A module 307 is executed when a database is designated as an output destination in the integrated transmission unit 304. A module 308 is executed when an MFP similar to the present MFP is designated as an output destination in the integrated transmission unit 304.

  Reference numeral 309 denotes a remote copy scan (remote-copy-scan) module, which outputs the output destination of image information read by the scanner 201 using the scanner function of the MFP 101 to a printer of another MFP connected via a network or the like. This module performs processing equivalent to the copy function realized by the MFP 101 alone. A remote copy print (Remote-Copy-Print) module 310 uses the printer function of the MFP 101 to obtain image information obtained by using image information read by a scanner of another MFP connected via a network or the like as an input source. This is a module that, by outputting, performs processing equivalent to the copy function realized by the MFP 101 alone. The box module 311 stores the scanned image or PDL print image in the HDD, prints the stored image using the printer function, transmits the image using the integrated transmission (Universal-Send) function, deletes the document stored in the HDD, and groups (to individual BOX). Management functions such as storage), movement between BOXes, and copying between BOXes. The box module 311 is provided with a communication function by the HTTP module 312 and the TCP / IP module 316.

  Reference numeral 312 denotes an HTTP module, which is used when the MFP performs communication using HTTP, and provides a communication function to the Web server module 303 and the Web pull print module 311 described above using a TCP / IP communication module 316 described later. Reference numeral 313 denotes an lpr module, which provides a communication function to the printer module 305 in the integrated transmission unit 304 described above by a TCP / IP communication module 316 described later. Reference numeral 314 denotes an SMTP module, which provides a communication function to the E-mail module 306 in the integrated transmission unit 304 by a TCP / IP communication module 316 described later. Reference numeral 315 denotes an SLM, that is, a Salutation-Manager module, which is connected to the database module 317, the DP module 318, the remote copy scan module 309, and the remote copy print module 310 in the integrated transmission unit 304 by a TCP / IP communication 316 module described later. Provides communication functions.

  A TCP / IP communication module 316 provides a network communication function to the various modules described above using a network driver 316 described later. Reference numeral 317 denotes a network driver that controls a portion physically connected to the network.

  A control API 318 provides an interface with an upstream module such as the integrated transmission unit 304 and a downstream module such as a job manager (Job-Manager) 319 described later. It will reduce the relationship and increase the applicability of each. A job manager 319 interprets processing instructed from the various modules described above via the control API 318, and gives instructions to each module (320, 324, 326) described later. The job manager 319 centrally manages various jobs executed in the MFP, including control of FAX jobs.

  Reference numeral 320 denotes a codec manager (CODEC-Manager) that manages and controls various compression / decompression of data in the process instructed by the job manager 319. Reference numeral 321 denotes an FBE encoder module (FBE-Encoder) which compresses data read by a scan process executed by a job manager 319 and a later-described scan manager (Scan-Manager) 324 in an FBE format. A JPEG codec module (322) is a JPEG codec module (JPEG-CODEC). The scan data executed by the job manager 319 and the scan manager 324 and the print processing executed by the print manager (Print-Manager) 326 are used for JPEG of the read data. JPEG expansion processing for compression and print data is performed. Reference numeral 323 denotes an MMR codec (MMR-CODEC). In the scan process executed by the job manager 319 and the scan manager 324 and the print process executed by the print manager 326, the data read from the scanner is compressed by MMR and sent to the printer. MMR expansion processing for print data to be output is performed.

  Reference numeral 324 denotes a scan manager (Scan-Manager) that manages and controls the scan processing instructed by the job manager 319. Reference numeral 325 denotes a SCSI driver, which communicates between the scan manager 324 and the scanner unit 201 to which the MFP is internally connected. Reference numeral 326 denotes a print manager (Print-Manager) that manages and controls print processing instructed by the job manager 319. Reference numeral 327 denotes an engine interface (Engine-I / F) that provides an I / F between the print manager 326 and the printer unit 202. A parallel port driver 328 provides an I / F when the Web pull print 311 outputs data to an output device (not shown) via the parallel port.

Next, details of the Address-Book 302 will be described. The Address-Book 302 is stored in a non-volatile storage device (non-volatile memory, hard disk, or the like) in the MFP 101, and in this, features of other devices connected to the network are described. For example, those listed below are included.
・ The official name and alias name of the device,
・ Device network address,
・ Network protocol that can be processed by equipment,
-Document formats that can be processed by the device,
・ Compression type that can be processed by equipment,
・ Image resolution that can be processed by the device,
-Paper size and paper source information for printer devices,
-Folder name capable of storing documents in the case of server (computer) device Each application described below can determine the characteristics of the delivery destination based on the information described in the above Address-Book 302.

  With reference to the Address-Book 302, the MFP 101 can transmit data. For example, the remote copy application discriminates the resolution information that can be processed by the device designated as the delivery destination from the Address-Book 302, and compresses the binary image read by the scanner using the known MMR compression in accordance therewith. Then, it is converted into a known TIFF (Tagged Image File Format), passed through the SLM 303, and transmitted to the printer device on the network. Although not described in detail, the SLM 303 is a type of network protocol including device control information called a known Salutation-Manager.

  Next, the hardware configuration of the host computer 102 in FIG. 1 will be described with reference to FIG. The host computer 102 has the configuration and functions of a general personal computer, and includes peripheral devices such as a monitor 401, a keyboard / mouse 402, a central processing unit CPU 403 that controls the entire host computer 102, and stores applications and data. A hard disk 405, a memory 406, and the like. Further, it is connected to a transmission medium such as the network 103 via the network interface 406.

  Next, the above-described PDF high compression will be described with reference to FIGS.

  High-PDF compression here is a color image compression technique, which performs region determination and performs compression by adaptively changing between binary compression by MMR and irreversible compression by JPEG according to the attribute of each region. Thus, the compression rate can be increased and the character area can be obtained with high quality.

  The input image (501), which is a multi-valued image, is binarized by the binarization unit (502), and a binary image (503) is generated. The region determination unit (504) receives the binary image (503) as an input, acquires a pixel block by, for example, contour tracking of a pixel (for example, a black pixel) of a predetermined value, and sets the size and position of the pixel block. A region is formed by grouping based on this, and a character region is determined based on the size and arrangement of pixel blocks in the formed region, thereby generating character region information. The character area information is information indicating the position and size of the character area. Further, the area determination unit (504) determines the character area, and the other part is determined as a photographic area indicating a natural (gradation) image such as a photograph, an illustration, or a background. The character cutout unit (505) cuts out each character (unit character region) in the character region as a character cut rectangle for the region determined by the region determination unit (504) as the character region, and generates character cut rectangle information To do. The character cut rectangle information is information indicating the position and size of the character cut rectangle. It is assumed that the character area information and the character cut rectangle information are managed as character area information (506). Also, a binary image (503) is input, and a partial binary image (507), which is a binary image for each character area, is generated for the area determined as a character area by the area determination unit (504).

  On the other hand, the input image (501) is reduced (or reduced in resolution) by the reduction unit (512), and a reduced multi-valued image (513) is generated. The representative color extraction unit (510) receives the partial binary image (507) as input, calculates the representative color of the character cut rectangle while referring to the character region information (506) and the reduced multi-value image (513), The result is managed as character color information (511) (refer to Patent Document 2 for details of this processing). The character area filling unit (514) receives the reduced multi-valued image (513) as input, and refers to each character area of the reduced multi-valued image (513) while referring to the character area information (506) and the partial binary image (507). Alternatively, a process of filling the character-cut rectangle with its peripheral color is performed (refer to Patent Document 1 for details of this process).

  After the above processing, each partial binary image (507) is compressed as a compressed code 1 (509) by the MMR compression unit (508). In addition, the multi-valued image filled in with the character region filling unit (514) is compressed as compressed code 2 (516) by the JPEG compression unit (515).

  Thus, the compressed data (517) obtained by combining the data groups of the character area information (506), the compressed code 1 (509), the character color information (511), and the compressed code 2 (516) obtained from each component is obtained. Generated. The compressed data (517) is further reversibly compressed with PDF or the like to generate PDF highly compressed data.

  FIG. 6 is a diagram showing a schematic configuration for expanding the compressed data (517) generated as described above. The MMR decompression unit (601) receives the compression code 1 (509) as input, performs MMR decompression processing, and generates a partial binary image (602). The JPEG decompression unit (605) receives the compression code 2 (516), performs JPEG decompression processing, and further performs enlargement processing in the enlargement unit (606), thereby generating a multi-value image (607). The synthesizing unit (603) assigns the character color information (511) to the black pixels of the partial binary image (602) while referring to the character area information (506), and selects the partial binary image to which the character color is assigned. It is synthesized and displayed on the value image (607). At this time, the white pixels of the partial binary image (602) are assigned a transparent color and pass through the multi-valued image (607).

  As described above, the image decompression device decompresses the compressed data generated by the image compression device and restores the image.

  FIG. 7 shows a schematic diagram of the images used or generated in FIGS. 5 and 6.

  Reference numeral 701 denotes an input image (501). Reference numeral 702 denotes a binary image (503).

  Reference numeral 703 denotes a result of the area determination performed by the area determination unit (504) in the character area and the photograph area. Here, it is assumed that 704 and 706 are determined as character areas, and 705 is determined as a photograph area.

  Reference numerals 707 and 708 denote partial binary images (507) of areas determined as character areas by the area determination unit (504).

  Reference numeral 709 denotes a schematic diagram of a character cut rectangle cut out by the character cutout unit (505). Reference numeral 710 denotes a character cut rectangle of the character region 704, and reference numerals 711 and 712 denote character cut rectangles of the character region 706. Here, as indicated by reference numerals 711 and 712, characters and photographs may be mixed in a character cut rectangle in the character area. For example, as in Patent Document 1, when a group of pixels is grouped based on the closeness of the position and the matching of the sizes, a photo area close to the character size may be mixed in the character area. When all of these rectangles are handled as characters, the rectangle that should be treated as a photograph, such as 712, is processed as a binary image, and information is lost. Reference numeral 713 denotes compressed data (517) or PDF high-compressed data generated when all the character-cut rectangles in the character area are handled as characters. Here, as shown at 714, a photographic region originally having gradation and color is treated as a character region and binarized, resulting in information loss.

  In order to solve these problems, in the present invention, an area determination unit 2 (801) is further provided as shown in FIG. Other components are the same as those in FIG.

  In order to solve these problems, in the present invention, an area determination unit 2 (801) is further provided as shown in FIG. Other components are the same as those in FIG.

  Next, the area determination unit 2 (801), which is the point of the present invention, will be described using the flowchart of FIG. Here, the flowchart of FIG. 9 is a part of the processing of FIG. 8, and therefore FIG. 8 is referred to as appropriate. Further, the area determination unit 2 (801) shows the processing of 907 to 912 in FIG.

  Next, the area determination unit 2 (801), which is the point of the present invention, will be described using the flowchart of FIG. Here, the flowchart of FIG. 9 is a part of the processing of FIG. Further, the area determination unit 2 (801) indicates the processing of 907 to 912 surrounded by a broken line 917 in FIG.

  First, in step 901, the binarization unit (502) binarizes the input image (501).

  Next, in step 902, the area determination unit (504) performs area determination on the binary image (503). The area determination in step 902 is performed by, for example, acquiring a pixel block by performing contour line tracking in a binary image and combining characters and character lines that have been divided by grouping nearby pixel blocks. Will be. Based on the size and positional relationship of the pixel block included in the region formed by the grouping, it is determined whether the region is a character region including one or more characters.

  In step 903, n, which is a counter for the number of areas, is initialized. Next, in step 904, if the attention area is an area determined to be a character area, the process proceeds to step 905. If the attention area is determined to be a non-character area, the process proceeds to step 912.

  In step 905, the character cutout unit (505) cuts out the character. For example, a character line can be cut out by taking a histogram in the horizontal direction, and a character rectangle can be cut out by taking a histogram in the vertical direction of each character line.

  In step 906, m, which is a counter for the number of rectangles for character cutting, is initialized.

  Next, in the area determination unit 2 (801), first, in step 907, the character cut rectangle cut out in step 905 is thinned (1).

  Here, the thinning (1) process will be specifically described.

  Thinning (1) is a process of thinning a character-cut rectangular area of an input binary image. In the thinning method, first, black pixels connected horizontally are detected, and one pixel at each of the left and right ends is deleted (replaced with white pixels). Next, vertically connected black pixels are detected, and one pixel at both the upper and lower ends is deleted. For example, a binary image 1401 shown in FIG. 14 becomes an image shown by 1402 by detecting a black pixel connected horizontally and deleting one pixel at both left and right ends. Next, an image shown in 1403 is obtained by detecting black pixels connected vertically and deleting one pixel at both upper and lower ends. The thinned image generated in this way is temporary for use in calculating a chromatic dispersion value, which will be described later, and thus is stored in a temporary storage area.

  In addition, an image after thinning processing (1) when a character-cut rectangle of a binary image such as 1101 in FIG. Reference numeral 1101 denotes a character-cut rectangle of the input binary image, and the character “P” is surrounded by a square frame. The width of the rectangular frame is 2 pixels wide as indicated by 1102, and the thickness of the character “P” is the pixel width indicated by 1103. Reference numeral 1104 denotes an image after thinning. When thinning is performed, the surrounding square frame is deleted as indicated by 1105, and the character “P” has the pixel width indicated by 1106.

  Next, in step 908, the character cut rectangle cut out in step 905 is thinned (2).

  Here, the thinning (2) process will be specifically described.

Thinning (2) is a process for thinning a character-cut rectangular area of an input binary image, as in thinning (1). Thinning (2) is different from thinning (1) in that the black pixels to be connected are detected and the number of pixels to be deleted is switched based on the number of connected pixels. The relationship between the number of connected pixels and the number of deleted pixels is shown below.
0 ≦ number of connected pixels ≦ 2: not deleted.
3 ≦ number of connected pixels ≦ 6: 1 pixel deleted at both ends 7 ≦ number of connected pixels: 2 pixels deleted at both ends For example, when thinning (2) is performed on the binary image 1401 shown in FIG. Based on the relationship of the number of deleted pixels, the image shown in 1404 is obtained by detecting the black pixels connected horizontally and deleting the pixels at both the left and right ends. Next, an image shown in 1405 is obtained by detecting black pixels connected vertically and deleting pixels at both upper and lower ends. The thinned image generated in this way is stored in a temporary storage area. Further, an image after the thinning process (2) is performed on a character cut rectangle such as 1101 in FIG. 11 is shown in 1107 in FIG. In thinning (2), the square frame is not deleted as indicated by 1108, and the character “P” has the pixel width indicated by 1109.

  As described above, in steps 907 and 908, thinning (1) and thinning (2) are performed. Note that if step 907 and step 908 can be executed in parallel, the processing time will be shortened, but the processing may be executed in order.

  Next, in step 909, chromatic dispersion values (ColorStd_1, ColorStd_2) are calculated for the respective images obtained by the above-described thinning (1) and thinning (2).

  Here, the color dispersion will be specifically described. The color dispersion in this embodiment is used as a reference for determining whether the color in the character cut rectangle is a single color (for example, black or red) or a plurality of colors (for example, a mixture of black and red). . For example, when the color dispersion value is small, it is determined that there is a high possibility of a single color, and when the color dispersion value is large, it can be determined that there are a plurality of colors. In the present embodiment, the color in the character cut rectangle uses the size of the color dispersion value as a criterion for determining whether it is a character or a photo. The illustration is based on the rule of thumb that there are many cases of multiple colors.

  Next, a method for calculating the chromatic dispersion value will be specifically described. The color information refers to the color of the reduced multi-value image (513) or the input image (501) before binarization processing (e.g., 8 bits for each RGB value). Further, preferably, a value obtained by converting the RGB value into luminance and luminance color difference information (for example, 8 bits for each YCbCr value) is referred to. Here, as an example, the chromatic dispersion value of the Cb value of the YCbCr value is calculated. Since the conversion method from RGB to YCbCr is well-known, description is abbreviate | omitted.

  First, based on the color (Cb value) of the reduced multi-valued image (513) corresponding to the position of the black pixel of the thinned image 1104 obtained by thinning (1), a distribution diagram of Cb values and appearance frequencies is generated. . For example, FIG. 12 is a diagram in which the black pixels in FIG. 11 are represented in units of pixels, and the distribution diagram is generated by sequentially referring to the Cb values of the pixels 1201 and 1202. A variance value is calculated based on the distribution map generated in this way.

The variance is a generally known variation, and is obtained by the following equation.
Variation (dispersion): Σ (Cb (i) −m) ² / n
N: Number of data (number of black pixels in the character cut rectangle)
Cb (i): Cb value of the reduced multi-valued image corresponding to the black pixel in the character-cut rectangle. M (average): ΣCb (i) / n
The chromatic dispersion value is calculated as described above. Here, chromatic dispersion values for the respective images obtained by thinning (1) and thinning (2) are ColorStd_1 and ColorStd_2, respectively. Further, here, as an example, the color dispersion value of the Cb value is calculated, but the color dispersion value may be calculated based on the Cr value, R, G, and B values. Further, after calculating the color dispersion value, the temporary storage area described above is initialized.

  Next, the reason why the chromatic dispersion is calculated from the thinned image will be described. As described above, the color dispersion refers to the reduced multi-value image (513) or the input image (501) before the binarization process. Thinning is performed at this time in order to reduce the influence of the quality of the color multilevel image to be referred to. That is, when the color multi-valued image is deteriorated due to factors such as compression or color misregistration, the original color dispersion value of characters in the character cut rectangle is affected.

Next, the reason for calculating chromatic dispersion from two types of thinned images will be described. Hereinafter, advantages and disadvantages of calculating chromatic dispersion from thinning (1) and thinning (2) will be described.
(A) When color dispersion is calculated from thinning (1) Advantages: Original color dispersion value of characters in a character-cut rectangle or a photograph, that is, a highly accurate color dispersion value can be obtained (however, the disadvantages will be described) Except exceptions).
Disadvantages: Since black pixels with a width of 2 pixels are deleted, the accuracy of the color dispersion value of an image with a large width of 2 pixels is low.

Here, the advantages of calculating the chromatic dispersion from the thinning (1) will be specifically described with reference to FIG. As described above, 1403 is an image when 1401 is thinned (1), and 1405 is an image when 1401 is thinned (2). Here, since 1403 in the case of thinning (1) remains only the core (inside) of the image, it is hardly affected by the quality of the color multi-valued image. On the other hand, in the case of thinning (2), 1405 is likely to be affected by these because the portion other than the core of the image remains.
(B) When Color Dispersion is Calculated from Thinning (2) Advantages: A highly accurate color dispersion value of a photograph having a width of two pixels compared to (A) can be obtained.
Disadvantages: The accuracy of chromatic dispersion values is lower than in (A).

  Here, the advantages of calculating the chromatic dispersion from the thinning (2) will be specifically described with reference to FIG. Reference numeral 1301 denotes a color multivalued image, in which a mobile phone of black (gradation) is drawn in a red round frame. Reference numeral 1302 denotes a binary image obtained by binarizing 1301, and a round frame indicates a black pixel having a width of two pixels. 1303 is an image when thinning (1) is performed on 1302, and 1304 is an image when thinning (2) is performed on 1302. Here, in the case of 1303 in the case of thinning (1), since the round frame portion is deleted, the color dispersion value becomes small. In the case of 1304 in the case of thinning (2), a round frame portion remains and the color dispersion value becomes large. Therefore, since the rectangle may not be determined as a photograph only by thinning (1), thinning (2) is necessary.

  Next, in step 910, the chromatic dispersion values (ColorStd_1, ColorStd_2) are compared with preset threshold values (th1, th2). If both ColorStd_1 and ColorStd_2 are smaller than the threshold value, it is determined as TEXT in step 911, and if either is larger than the threshold value, it is determined as IMAGE in step 912. Next, in step 913, the character cut rectangle count m is compared with the character cut rectangle number M. When steps 907 to 912 are completed for all the character cut rectangles, the flow proceeds to step 914, while unprocessed In step 915, the counter m is incremented and processing is performed on the next character cut rectangle. In step 914, the counter n for the number of areas is compared with the number N for the areas. When all the areas have been processed, this process is terminated. If there is an unprocessed area, the counter n is counted in step 916. Is increased to process the next area.

  As described above, the area determination unit 2 (801) determines whether the character cut rectangle in the character area is TEXT or IMAGE using the chromatic dispersion value.

  For example, when the input image (501) is 701 in FIG. 7, a schematic diagram of a result determined by the region determination unit 2 (801) is shown in 1001 in FIG. Further, reference numeral 802 in FIG. 8 indicates that, based on this result, a partial binary image is generated using a character-cut rectangular area in which the area is a character area and the result of the area determination unit 2 (801) is TEXT. Yes. For example, 1002 is a schematic diagram of a partial binary image of a text area generated when the input image (501) is 701 in FIG. When the processing of the area determination unit 2 is performed, the TEXT 711 and the IMAGE 712 are identified. When the generated compressed data (517) or PDF high-compressed data is reproduced, the result is 1003.

  As described above, the area determination unit 2 (801) can accurately determine whether the character cut rectangle is a character or a photograph based on the color dispersion value of the character cut rectangle by two types of thinning methods. By applying this determination result to compression, it is possible to obtain compressed data (517) with good image quality or PDF high-compression data.

(Example 2)
In the first embodiment, the case where it is determined whether the character cut rectangle is a character or a photo after calculating the color dispersion value of the character cut rectangle by two types of thinning methods has been described. In the second embodiment, it is further determined whether or not to calculate the color dispersion value of the character-cutting rectangle by the second thinning method, based on the color dispersion value of the character-cutting rectangle by the first thinning method.

  Hereinafter, the region determination method according to the second embodiment will be described with reference to the flowchart of FIG.

  FIG. 15 shows an area determination unit 2 (801) corresponding to 917 in FIG. 9 used in the first embodiment. First, in step 1501, thinning (1) described in the first embodiment is performed. Next, in step 1502, a chromatic dispersion value (ColorStd_1) is calculated. Next, in step 1503, the chromatic dispersion value (ColorStd_1) is compared with a preset first threshold th1. If it is smaller than the threshold value, the process proceeds to step 1504. If it is larger than the threshold value, it is determined in step 1508 that the image is IMAGE. In step 1504, the thinning (2) described in the first embodiment is performed. Next, in step 1505, a chromatic dispersion value (ColorStd_2) is calculated. Next, in step 1506, the chromatic dispersion value (ColorStd_2) is compared with a preset second threshold th2. If it is smaller than the threshold value, it is determined in step 1507 that the text is TEXT. If it is greater than the threshold, it is determined in step 1508 that IMAGE.

  As described above, by determining whether to calculate the color dispersion value of the character-cut rectangle by the second thinning method based on the color dispersion value of the character-cut rectangle by the first thinning method, While maintaining the same accuracy as 1, higher-speed region determination is possible.

(Example 3)
In the first and second embodiments, the example in which the region determination method is used in the image compression technique has been described. In the third embodiment, an example in which this region determination method is used when an optical character recognition device (OCR) technique is used will be described.

  As described above, in the OCR process, a character line is cut out (extracted) by taking a density projection (histogram) on the document image, and further, a character block cut out (extracted) in units of one character. Then, the feature data is extracted from each character block, the similarity with the standard pattern is calculated, and the character with the highest similarity is output as the recognition result. That is, up to the character block cutout (extraction) process means binarization, area determination, and character cutout as described above with reference to FIG. In addition, as described above, when the character cut rectangle is non-character, if character recognition is performed on the non-character, the overall processing speed is reduced, and a meaningless text code is output. This is not preferable.

  Here, in the third embodiment, before performing the OCR process, the character cut rectangle area is determined in advance to determine whether it is a character or a non-character, and the OCR process is performed only when the character is determined. To solve these problems. This process is shown in the flowchart of FIG. In FIG. 16, the processing portions 901 to 916 are the same as those in FIG. In step 910, the color dispersion values (ColorStd_1, ColorStd_2) are compared with preset threshold values (th1, th2). If both ColorStd_1 and ColorStd_2 are smaller than the threshold values, TEXT is determined in step 911. Therefore, OCR processing is performed in step 1601, and a character recognition result is output. If any of the values is larger than the threshold value, it is determined as IMAGE in step 910, and thus the OCR process is not performed.

  As described above, when the OCR technique is used, unnecessary OCR processing is not performed, so that the processing speed is improved, and it is possible to suppress the output of meaningless text codes.

Example 4
The present invention can be applied to a system composed of a plurality of devices (for example, a host computer, an interface device, a reader, a printer, etc.) or an apparatus composed of a single device (for example, a copier, a facsimile machine, etc.). May be.

  Another object of the present invention is to supply a recording medium recording a program code of software that implements the functions of the above-described embodiments to a system or apparatus, and the computer (or CPU or MPU) of the system or apparatus records the recording medium. Needless to say, this can also be achieved by reading and executing the program code stored in. In this case, the program code itself read from the recording medium realizes the novel function of the present invention, and the storage medium storing the program code constitutes the present invention. As a storage medium for supplying the program code, for example, a floppy (registered trademark) disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, nonvolatile memory card, ROM, or the like is used. be able to.

  Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also the OS running on the computer based on the instruction of the program code performs the actual processing. Needless to say, a case where the function of the above-described embodiment is realized by performing part or all of the processing is also included.

  Further, after the program code read from the recording medium is written in the memory provided in the extension function board inserted in the computer or the function extension unit connected to the computer, the function extension is performed based on the instruction in the program code. It goes without saying that the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing, and the functions of the above-described embodiments are realized by the processing.

  As described above, according to the present invention, it is possible to improve the region determination accuracy. In addition, since it is possible to execute suitable region determination for characters and non-characters, it is possible to obtain good image quality and improve compression efficiency. Further, when using the OCR technology, it is possible to improve the recognition rate by suppressing the output of meaningless text codes as well as the processing speed.

Schematic diagram of the system in the first embodiment Hardware configuration of MFP in the first embodiment Software configuration of MFP in the first embodiment Hardware configuration of PC in the first embodiment Block diagram of image compression apparatus 1 Block diagram of image decompression device Sample of input image to output image Block diagram of an image compression apparatus according to the present invention 2 Flow chart of area determination in the first embodiment Sample 2 of input image to output image in the first embodiment Explanation of thinning and chromatic dispersion 1 Explanation of thinning and chromatic dispersion 2 Explanation of thinning and chromatic dispersion 3 Illustration of thinning Flow chart of region determination in the second embodiment Flow chart of area determination in the third embodiment

Claims (14)

Binarization means for generating a binary document image from a multi-valued document image;
Area extracting means for extracting a determination target area from the generated binary image;
First thinning means for thinning the image of the determination target region by a first thinning process;
Second thinning means for thinning the image of the determination target area by a second thinning process;
First color dispersion value calculating means for calculating a first color dispersion value of the multi-value document image with respect to a position corresponding to a thinning result by the first thinning means;
Second color dispersion value calculating means for calculating a second color dispersion value of the multi-value document image with respect to a position corresponding to a thinning result by the second thinning means;
An image processing apparatus comprising: area determination means for determining whether the determination target area is a character or a photograph based on the calculated first color dispersion value and second color dispersion value. A pre-determination unit that determines a character region candidate from the image based on a pixel block included in the document image;
The image processing apparatus according to claim 1, wherein the region extraction unit extracts a rectangular region of a character included in the character region candidate determined by the pre-determination unit as the determination target region.   The first thinning means detects black pixels connected in the horizontal direction and deletes a predetermined number of pixels at both left and right ends, and further detects black pixels connected in the vertical direction to detect a predetermined number of pixels at the upper and lower ends. The image processing apparatus according to claim 1, wherein a thinned image is obtained by deleting pixels.   The second thinning means detects a black pixel connected in the horizontal direction, performs a deletion process on both left and right pixels in accordance with the number of the connected black pixels, and further adds a black pixel connected in the vertical direction. The image processing apparatus according to claim 1, wherein a thinned image is obtained by detecting and executing deletion processing of pixels at both upper and lower ends according to the number of connected black pixels.   The second thinning means does not delete when the number of connected black pixels is 2 or less, deletes one pixel at both ends when the number of connected black pixels is 3 to 6, and the number of connected black pixels is 7 5. The image processing apparatus according to claim 4, wherein two pixels at both ends are deleted in the above case.   The area determination unit is configured to output the calculated first color dispersion value smaller than a predetermined first threshold value and the calculated second color dispersion value smaller than a predetermined second threshold value. The image processing apparatus according to claim 1, wherein the determination target region is determined as a character.   Compression that obtains compressed data of the document image by performing a first compression process on the area determined to be a character by the area determination means and performing a second compression process on the area determined to be a photograph The image processing apparatus according to claim 1, further comprising means.   The image processing apparatus according to claim 7, wherein the first compression process is a compression process suitable for a binary image, and the second compression process is a compression process suitable for a multi-valued image. An image processing apparatus further comprising representative color calculation means for calculating a representative color from an area determined as a character by the area determination means,
The compressed data includes a first compressed code obtained by the first compression process, a second compressed code obtained by the second compression process, and representative color information obtained by the representative color calculation means. The image processing apparatus according to claim 7, wherein the image processing apparatus is included.   8. The document image to be subjected to the second compression processing is a document image generated by filling a portion of an area determined as the character area in the document image with a surrounding color. An image processing apparatus according to 1.   The image processing apparatus according to claim 1, further comprising a character recognition processing unit that performs a character recognition process on the region determined to be a character by the region determination unit. A binarization step for generating a binary document image from a multi-value document image;
A region extracting step of extracting a determination target region from the generated binary image;
A first thinning step of thinning the image of the determination target region by a first thinning process;
A second thinning step of thinning the image of the determination target region by a second thinning process;
A first color dispersion value calculating step of calculating a first color dispersion value of the multi-value document image with respect to a position corresponding to a thinning result obtained by the first thinning step;
A second color dispersion value calculating step of calculating a second color dispersion value of the multi-value document image with respect to a position corresponding to the thinning result by the second thinning step;
An image processing method comprising: an area determination step for determining whether the determination target area is a character or a photo based on the calculated first color dispersion value and second color dispersion value. A computer program for controlling an image processing apparatus for processing a document image,
A binarization step for generating a binary document image from a multi-value document image;
A region extracting step of extracting a determination target region from the generated binary image;
A first thinning step of thinning the image of the determination target region by a first thinning process;
A second thinning step of thinning the image of the determination target region by a second thinning process;
A first color dispersion value calculating step of calculating a first color dispersion value of the multi-value document image with respect to a position corresponding to a thinning result obtained by the first thinning step;
A second color dispersion value calculating step of calculating a second color dispersion value of the multi-value document image with respect to a position corresponding to the thinning result by the second thinning step;
Program code for causing a computer to execute each step of determining whether the determination target region is a character or a photo based on the calculated first color dispersion value and second color dispersion value A computer program comprising:   A computer-readable storage medium storing the computer program according to claim 13.

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