JP2008154216A - Image processing method and device, image forming device, document reading device, computer program, and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide: an image processing method capable of determining propriety of processing such as copying more precisely than before when determining similarity of document images; an image processing device; an image forming device and a document reading device having the image processing device; a computer program for achieving the image processing device; and a recording medium in which the computer program is recorded. <P>SOLUTION: A document matching process section 22 calculates feature points (e.g., the centroid) on the basis of an input document image, then selects a plurality of feature points from among the calculated feature points, and calculates a hash value on the basis of the selected feature points. Then, on the basis of the calculated features, the document matching process section 22 determines whether the document image is similar to a preliminarily stored registration format (registered image). When it is determined as being similar, the document matching process section determines whether write-in is present in the document image, and outputs a determination signal (a determination result indicating the presence or absence of write-in). <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention provides an image processing method, an image processing apparatus, an image forming apparatus including the image processing apparatus, an original reading apparatus, and an image processing method for determining whether an acquired original image is similar to a registered image stored in advance. The present invention relates to a computer program for realizing an image processing apparatus and a recording medium on which the computer program is recorded.

  As a process of reading an original with a scanner and determining whether the read original image is similar to a registered image stored in advance, a method of extracting a keyword from an image read with an OCR and performing pattern matching with the extracted keyword Alternatively, a method has been proposed in which a document to be determined is limited to a form document with ruled lines, and pattern matching is performed based on information about ruled lines extracted from the read document.

  In addition, important documents or confidential documents are stored in advance as registered images, and when it is determined that the read original image is similar to the registered image, copying of the read original is prohibited to prevent forgery. Has been done to improve security.

  However, since the pattern matching method is used for similarity determination, even if the original image is subjected to processing such as color conversion, enlargement / reduction, or trimming on the read original image, There is a problem that it is determined that the image is similar to the registered image, and the original cannot be copied.

Therefore, when it is determined that the input image (original image) is an image for which image formation (for example, copying) is prohibited, faithful formation of the input image is prohibited and is performed according to a user instruction. When the degree of editing processing exceeds a predetermined level, the edited input image is faithfully formed, so that even a manuscript that is prohibited from being copied can be copied together with various copying functions provided in the copying machine. If the result is clearly distinguishable from the original, a copying apparatus that performs a copying operation has been proposed (see Patent Document 1).
Japanese Patent No. 3028490

  However, in the apparatus of Patent Document 1, when the degree of editing processing is greater than or equal to a predetermined level, the copying operation of the document is unconditionally performed. Alternatively, when confidential information or the like is written, there is a problem that the manuscript is copied although it is necessary to prohibit copying or electronic distribution of the manuscript. In particular, when there are many writings on a manuscript, it is clearly distinguishable from the manuscript before writing, and there is a high possibility of copying, and it has been desired to accurately determine whether copying or the like is possible. In addition, when important matters, personal information, confidential information, etc. are written on the manuscript, it is desirable to prevent the written important matters, personal information, confidential information, etc. from being illegally copied or leaked. It was. Further, when there are various types of originals, there has been a demand for easily obtaining an original that has the same format as an already written original and is not written.

  The present invention has been made in view of such circumstances. When it is determined that the document image is similar to a registered image stored in advance, it is determined whether or not the acquired document image is written. An image processing method, an image processing apparatus, and an image processing apparatus capable of determining whether or not processing such as copying can be performed more accurately than in the past by controlling output processing for an acquired document image or registered image according to the result Image forming apparatus and document reading apparatus, a computer program for realizing the image processing apparatus, and a recording medium on which the computer program is recorded.

  Another object of the present invention is to prohibit unauthorized output of important information, personal information, confidential information, etc. written on a manuscript by prohibiting the output of the obtained manuscript image when it is determined that writing has been performed. An object of the present invention is to provide an image processing method, an image processing apparatus, an image forming apparatus including the image processing apparatus, and a document reading apparatus capable of preventing copying or outflow.

  Another object of the present invention is to provide a portion corresponding to one or a plurality of predetermined portions for a registered image similar to the original image of the acquired original image when it is determined that writing has been performed. Providing an image processing method, an image processing apparatus, an image forming apparatus provided with the image processing apparatus, and a document reading apparatus capable of preventing unauthorized copying or outflow of confidential information more finely by prohibiting the output of writing There is to do.

  Another object of the present invention is to permit the output of a registered image determined to be similar to the acquired document image when it is determined that the document is written, so that the same format as the document being written is permitted. An object of the present invention is to provide an image processing method, an image processing apparatus, an image forming apparatus including the image processing apparatus, and an original reading apparatus that can easily obtain a document without writing.

  Another object of the present invention is to record the acquired original image or the portion where writing is performed when it is determined that writing is performed, so that important matters, personal information, confidential information written on the original are recorded. An object of the present invention is to provide an image processing method, an image processing apparatus, an image forming apparatus provided with the image processing apparatus, and an original reading apparatus that can easily track and collate information when information or the like leaks.

  Another object of the present invention is to allow the user to output the acquired document image when it is determined that writing has not been performed (permit output of a document image that is not very important). An image processing method, an image processing apparatus, an image forming apparatus provided with the image processing apparatus, and a document reading apparatus can be provided.

  Another object of the present invention is to provide an image processing apparatus capable of determining whether or not writing has been performed more accurately by determining whether or not writing has been performed based on a document image from which the background has been removed. An object of the present invention is to provide an image forming apparatus and a document reading apparatus provided with a processing device.

  Another object of the present invention is to obtain the pixel value of each pixel in the first pixel block including the target pixel of the acquired document image, and the pixel value of each pixel in the second pixel block including the pixel. And whether or not the target pixel is an edge pixel is determined according to the calculated pixel value distribution of each pixel in the first pixel block, and based on the determined number of edge pixels An image processing method capable of accurately determining whether or not a document image is written without comparing the pixel values of the document image and the registered image by determining whether or not the acquired document image is written; An object of the present invention is to provide an image forming apparatus and an original reading apparatus including an image processing apparatus.

  Another object of the present invention is to calculate the pixel value of each pixel in the first pixel block including the target pixel for each color component and determine whether the target pixel is an edge pixel. An object of the present invention is to provide an image processing method capable of improving the determination accuracy of the presence / absence of writing, an image forming apparatus including the image processing apparatus, and a document reading apparatus.

  An image processing method according to the present invention is an image processing method for determining whether or not an acquired original image is written, and determines whether or not the acquired original image is similar to a registered image stored in advance. If it is determined that the document image is similar to the registered image, it is determined whether or not the acquired document image is written, and output processing for the acquired document image or registered image is controlled according to the determination result. Features.

  The image processing method according to the present invention is characterized in that, when it is determined that writing has been performed, the output of the acquired document image is prohibited.

  In the image processing method according to the present invention, when it is determined that writing has been performed, writing of locations corresponding to one or more locations determined for a registered image similar to the document image of the acquired document image Is prohibited from being output.

  The image processing method according to the present invention is characterized in that, when it is determined that writing has been performed, output of a registered image determined to be similar to the acquired document image is permitted.

  The image processing method according to the present invention is characterized in that, when it is determined that writing has been performed, the acquired document image or a portion where writing has been performed is recorded.

  The image processing method according to the present invention is characterized in that, when it is determined that writing has not been performed, output of the acquired document image is permitted.

  An image processing apparatus according to the present invention is an image processing apparatus for determining whether or not an acquired original image is written, and means for determining whether or not the acquired original image is similar to a registered image stored in advance. If the document image is determined to be similar to the registered image, a determination unit that determines whether or not the acquired document image is written, and the acquired document image or registration according to the determination result of the determination unit And control means for controlling output processing for the image.

  The image processing apparatus according to the present invention is characterized in that the control unit prohibits output of the acquired document image when it is determined that the writing is performed by the determination unit.

  The image processing apparatus according to the present invention includes means for predetermining one or a plurality of locations of a registered image, and the control means determines that the acquired original image is written when the determination means determines that writing has been performed. It is characterized in that the output of writing at a location corresponding to a location defined for a registered image similar to a document image is prohibited.

  The image processing apparatus according to the present invention is characterized in that the control unit permits output of a registered image determined to be similar to the acquired document image when the determination unit determines that writing is performed. To do.

  The image processing apparatus according to the present invention includes a recording unit that records the acquired original image or a portion where writing is performed when the determination unit determines that writing is performed.

  The image processing apparatus according to the present invention is characterized in that the control unit permits the output of the acquired document image when the determination unit determines that writing is not performed.

  The image processing apparatus according to the present invention includes a background removal unit that performs background removal on the acquired document image, and whether the determination unit performs writing based on the document image from which the background is removed by the background removal unit. It is characterized by determining whether or not.

  The image processing apparatus according to the present invention converts the pixel value of each pixel in the first pixel block including the target pixel of the acquired document image to the pixel value of each pixel in the second pixel block including the pixel. A pixel value calculation unit that calculates based on the pixel value and a distribution of pixel values of each pixel in the first pixel block calculated by the pixel value calculation unit determines whether or not the pixel of interest is an edge pixel And an edge determination unit configured to determine whether the acquired document image is written based on the number of edge pixels determined by the edge determination unit. It is characterized by.

  The image processing apparatus according to the present invention is configured such that, when the obtained document image is a color image, each of the pixel value calculation unit and the edge determination unit performs processing for each color component. .

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

  An original reading apparatus according to the present invention includes an original reading means for reading an original image and an image processing apparatus according to any one of the aforementioned inventions, and the original image read by the original reading means is read by the image processing apparatus. It is comprised so that it may process.

  A computer program according to the present invention is a computer program for causing a computer to determine whether or not an acquired original image is written, and whether or not the computer is similar to a registered image in which the original image is stored in advance. And a computer functioning as a means for determining whether or not a document image determined to be similar is written.

  A recording medium readable by a computer according to the present invention records the computer program according to the above-described invention.

  In the present invention, when it is determined that the acquired document image is similar to the registered image stored in advance, it is determined whether or not the acquired document image is written. Output processing (for example, copying, electronic distribution, facsimile, recording, etc.) for the acquired document image or registered image is controlled (for example, permitted or prohibited) according to the presence / absence of writing (determination result). By determining whether or not the acquired document image is written, it is possible to accurately determine whether or not processing such as copying is possible, as compared to simply determining whether or not the document image and the registered image are similar. be able to. In the present invention, an acquired document image is a document image obtained by reading a document image with a scanner, a document image in an electronic data format created by writing necessary items in data in a predetermined format using a computer, a scanner This means digitized data created by converting the data read in step 1 into a predetermined file format such as JPEG.

  In the present invention, when it is determined that the acquired document image is written, output of the acquired document image (for example, copying, electronic distribution, facsimile, recording, etc.) is prohibited. As a result, it is possible to prevent unauthorized copying or outflow of important matters, personal information, confidential information, etc. written on the manuscript.

  Further, in the present invention, when it is determined that writing has been performed, a location corresponding to one or a plurality of locations predetermined for a registered image similar to the document image of the acquired document image is determined. Writing output (for example, copying, electronic distribution, facsimile, recording, etc.) is prohibited. As predetermined locations, for example, in a description column of a document (registered image) with a predetermined format, a location where important matters, personal information, confidential information, and the like are written is determined in advance according to the type of the document. Also, the location can be determined in advance by a user operation. As a result, it is possible to prevent unauthorized copying or outflow of a portion where confidential information or the like is written. Further, a portion where information other than confidential information or the like is written can be used by the user by outputting the information as it is.

  Further, in the present invention, when it is determined that the acquired document image is written, an output of a registered image determined to be similar to the acquired document image (for example, copying, electronic distribution, facsimile, recording) Etc.) As a result, it is possible to easily obtain a document that has the same format as a document that has been written and that has not been written, from a wide variety of documents (registered images).

  Further, in the present invention, when it is determined that the acquired original image is written, the acquired original image or the portion where the original is written is recorded. This makes it possible to easily track and collate information when important matters, personal information, confidential information, etc. written on the manuscript are leaked.

  In the present invention, if it is determined that the acquired original image is not written, output of the acquired original image (for example, copying, electronic distribution, facsimile, recording, etc.) is permitted. When important items, personal information, confidential information, etc. are not written on the manuscript, the manuscript output is permitted (the manuscript image that is not so important is permitted to be outputted), thereby improving the user convenience. be able to.

  In the present invention, background removal is performed on the acquired document image, and it is determined whether writing is performed based on the document image from which the background is removed. As a result, even when the read original paper is different from the registered image, such as recycled paper or lightly colored paper, it is possible to accurately determine whether or not writing is performed.

  In the present invention, the pixel value of each pixel in the first pixel block including the target pixel of the acquired document image is changed to the pixel value of each pixel in the second pixel block including the pixel. Calculate based on Based on the calculated distribution of pixel values of each pixel in the first pixel block, it is determined whether or not the target pixel is an edge pixel. Based on the determined number of edge pixels, Determine whether there is a write. Thereby, it is not necessary to compare the pixel values of the document image and the registered image, and it is possible to accurately determine whether or not the document image has been written using only the acquired document image.

  In the present invention, when the acquired document image is a color image, the calculation of the pixel value of each pixel in the first pixel block including the target pixel for each color component and the target pixel are edge pixels. It is determined whether or not. As a result, it is possible to suppress the influence of isolated points in the original image, noise during reading, or dirt on the background of the original or dust adhering to the background. Further improvement can be achieved.

  In the present invention, when it is determined that the document image is similar to the registered image stored in advance, it is determined whether or not the acquired document image is written, and the acquired document image is determined according to the determination result. Alternatively, by controlling the output processing for the registered image, it is possible to determine whether or not processing such as copying can be performed more accurately than in the past. In this specification, “writing” means, for example, (1) a form entered in a fixed format such as a form with a pencil or a pen, and (2) a predetermined entry is input using a computer system or the like. This means that an entry is output by a printer or the like on a sheet on which a format is printed, and (3) a character or the like is input from a computer system or the like into a standard electronic format and output by a printer or the like.

  Further, in the present invention, when it is determined that writing has been performed, unauthorized copying of important matters, personal information, confidential information, etc. written on the manuscript is prohibited by prohibiting output of the obtained manuscript image. Or the outflow can be prevented.

  Further, in the present invention, when it is determined that writing has been performed, a location corresponding to one or a plurality of locations predetermined for a registered image similar to the document image of the acquired document image is determined. By prohibiting the output of writing, unauthorized copying or outflow of confidential information or the like can be prevented more finely.

  Further, in the present invention, when it is determined that writing has been performed, output of a registered image determined to be similar to the acquired document image is permitted, so that the same format as that of the document being written is allowed. A manuscript that has no writing can be easily obtained. In particular, when a large number of different formats (formats) are registered, a required format can be easily selected, and user convenience is improved.

  Further, in the present invention, when it is determined that writing has been performed, important items, personal information, confidential information written on the manuscript are recorded by recording the obtained manuscript image or the portion being written. Information can be easily tracked and collated when such information is leaked.

  Further, according to the present invention, when it is determined that writing has not been performed, output of the acquired document image is permitted (output is permitted for a document image that is not very important), thereby improving user convenience. Can be improved.

  Further, in the present invention, by removing the background of the original image before determining whether or not the acquired original image is written, the original paper to be read is recycled paper or lightly colored. Even if it is different from the registered image, such as paper, the presence / absence of writing can be accurately determined.

  In the present invention, the pixel value of each pixel in the first pixel block including the target pixel of the acquired document image is changed to the pixel value of each pixel in the second pixel block including the pixel. Based on the calculated distribution of pixel values of each pixel in the first pixel block, it is determined whether the pixel of interest is an edge pixel, and based on the determined number of edge pixels, It is determined whether or not the acquired document image has been written. Thereby, it is not necessary to compare the pixel values of the document image and the registered image, and it is possible to accurately determine whether or not the document image has been written using only the acquired document image.

  In the present invention, when the acquired document image is a color image, the calculation of the pixel value of each pixel in the first pixel block including the target pixel for each color component and the target pixel are edge pixels. By determining whether or not, it is possible to suppress the influence of isolated points in the original image, noise during reading, or dirt on the background of the original or dust adhering to the background, The determination accuracy of the presence / absence of writing can be further improved.

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

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

  The color image processing apparatus 2 includes a CPU, an application specific integrated circuit (ASIC), and the like.

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

  The shading correction unit 21 performs correction processing for removing various distortions generated in the illumination system, imaging system, imaging system, and the like of the color image input apparatus 1 on the input RGB signal. Further, the shading correction unit 21 performs processing for converting the signal such as a density signal into a signal that can be handled easily by the image processing system employed in the color image processing apparatus 2, and outputs the corrected RGB signal (image) to the document matching processing unit 22. Output.

  The document matching processing unit 22 binarizes the input image, calculates a feature point (for example, the center of gravity) of the connected region specified based on the binary image, and a plurality of feature points from the calculated feature points Is selected, an invariant is obtained based on the selected feature point, and a feature amount (for example, a hash value) is calculated based on the invariant. When the document collation processing unit 22 determines whether the input image (original image) based on the calculated feature amount is similar to a registered format (registered image) stored in advance, It is determined whether or not the input image (original image) has been written, and a determination signal (determination result indicating the presence or absence of writing) is output. In addition, the document collation processing unit 22 outputs the input RGB signal as it is to the subsequent input tone correction unit 23.

  In this specification, “writing” means, for example, (1) a form entered in a fixed format such as a form with a pencil or a pen, and (2) a predetermined entry is input using a computer system or the like. This means that an entry is output by a printer or the like on a sheet on which a format is printed, and (3) a character or the like is input from a computer system or the like into a standard electronic format and output by a printer or the like. In the present invention, an original image is an original image obtained by reading an original image with a scanner, an original image in an electronic data format created by writing necessary items into data in a predetermined format using a computer, and read with a scanner. It means digitized data created by converting the obtained data into a predetermined file format such as JPEG.

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

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

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

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

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

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

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

  The gradation reproduction processing unit 29 performs predetermined processing on the CMYK signal input from the output gradation correction unit 28 based on the region identification signal input from the region separation processing unit 24. For example, the tone reproduction processing unit 29 performs binarization processing or the like so as to be suitable for reproduction of high-frequency components in the color image output device 3 in order to improve the reproducibility of the area separated into character areas, particularly black characters or color characters. Multilevel processing is performed.

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

  The color image processing apparatus 2 temporarily stores the image data (CMYK signal) processed by the gradation reproduction processing unit 29 in a storage unit (not shown), and stores the image data in the storage unit at a predetermined timing for image formation. And the read image data is output to the color image output device 3. These controls are performed by a CPU (not shown), for example. A CPU (not shown) in the color image processing apparatus 2 performs output processing (for example, copying, electronic distribution, facsimile, recording, etc.) on the document image based on the determination signal output from the document matching processing unit 22. Control.

  FIG. 2 is a block diagram showing a configuration of the document matching processing unit 22. The document collation processing unit 22 includes a feature point calculation unit 221, a feature amount calculation unit 222, a voting processing unit 223, a similarity determination processing unit 224, a write determination processing unit 225, a memory 226, a control unit 227 that controls the above-described units, and the like. It has.

  The feature point calculation unit 221 performs predetermined processing to be described later on the input image (original image), extracts a character string or ruled line connection region of the input image, and extracts feature points (for example, the center of gravity of the connection region). ) Is calculated. More specifically, the feature point calculation unit 221 binarizes the input image, and the feature points of the connected area specified based on the binary image (for example, the binary image of each pixel constituting the connected area). (The value obtained by dividing the accumulated coordinate value by the number of pixels included in the connected region) is extracted (calculated), and the extracted feature point is output to the feature amount calculation unit 222.

  FIG. 3 is a block diagram illustrating a configuration of the feature point calculation unit 221. The feature point calculation unit 221 includes an achromatic processing unit 2210, a resolution conversion unit 2211, a filter processing unit 2212, a binarization processing unit 2213, a centroid calculation unit 2214, and the like.

If the input image is a color image, the achromatic processing unit 2210 achromatically converts the color image into a luminance signal or a brightness signal, and outputs the converted image to the resolution conversion unit 2211. For example, the luminance signal Yj can be expressed as Yj = 0.30 × Rj + 0.59 × Gj + 0.11 × Bj, where Rj, Gj, and Bj are the color components of each pixel RGB. Note that the RGB signal is not limited to the above formula, and can be converted into a CIE1976L ^* a ^* b ^* signal.

  Even if the input image is optically scaled by the color image input device 1, the resolution conversion unit 2211 scales the input image again so as to obtain a predetermined resolution, and the scale is scaled. The image is output to the filter processing unit 2212. As a result, even when scaling processing is performed in the color image input apparatus 1 and the resolution changes, feature points can be extracted without being affected by the change, and the originals can be classified with high accuracy. . In particular, in the case of a reduced character or the like, when binarization processing is performed and a connected region is specified, the character is crushed and the originally separated regions are connected and the calculated center of gravity is shifted. Fear can be prevented. The resolution conversion unit 2211 converts the resolution to a resolution smaller than the resolution read at the same magnification by the color image input apparatus 1. For example, an image read at 600 dpi (dot per inch) in the color image input device 1 is converted to 300 dpi. Thereby, the processing amount in the latter stage can be reduced.

  The filter processing unit 2212 corrects the spatial frequency characteristics of the input image (for example, image enhancement processing and smoothing processing), and outputs the corrected image to the binarization processing unit 2213. Since the spatial frequency characteristics of the color image input device 1 are different for each model, the filter processing unit 2212 corrects the different spatial frequency characteristics to the required characteristics. An image (for example, an image signal) output from the color image input apparatus 1 includes optical parts such as a lens or a mirror, aperture aperture of a light receiving surface of a CCD, transfer efficiency, afterimage, integration effect by physical scanning, and scanning. Deterioration such as image blur occurs due to unevenness. The filter processing unit 2212 repairs deterioration such as blurring that has occurred in an image by performing enhancement processing such as a boundary or an edge. Further, the filter processing unit 2212 performs a smoothing process for suppressing high-frequency components that are not necessary for the feature point extraction process to be processed later. As a result, feature points can be extracted with high accuracy, and as a result, the similarity of images can be determined with high accuracy. Note that the filter coefficient used in the filter processing unit 2212 can be appropriately set according to the model or characteristics of the color image input apparatus 1 used.

  FIG. 4 is an explanatory diagram illustrating an example of the filter coefficient of the filter processing unit 2212. As shown in the figure, the spatial filter is, for example, a mixed filter having a size of 7 × 7 (7 rows, 7 columns) and performing enhancement processing and smoothing processing. The pixels of the input image are scanned, and arithmetic processing using a spatial filter is performed on all the pixels. Note that the size of the spatial filter is not limited to 7 × 7, and may be 3 × 3, 5 × 5, or the like. The numerical value of the filter coefficient is an example, and is not limited thereto, and can be set as appropriate according to the model or characteristics of the color image input apparatus 1 used.

  The binarization processing unit 2213 binarizes the image by comparing the luminance value (luminance signal) or lightness value (lightness signal) of the input image with a threshold value, and the binarized binary image To 2214.

  Based on the binarization information (for example, represented by “1” and “0”) of each pixel of the binary image input from the binarization processing unit 2213, the center-of-gravity calculation unit 2214 applies to each pixel. Then, labeling (labeling processing) is performed, a connected region where pixels with the same label are connected is specified, the center of gravity of the specified connected region is extracted as a feature point, and the extracted feature point is sent to the feature amount calculation unit 222. Output. The feature points can be represented by coordinates (x ′, y ′) in the binary image (original image).

  FIG. 5 is an explanatory diagram illustrating an example of feature points of a connected region. In the figure, the specified connected area is the letter “A”, and is specified as a set of pixels with the same label. The feature point (centroid) of the character “A” is a position (x ′ coordinate, y ′ coordinate) indicated by a black circle in the drawing.

  FIG. 6 is an explanatory diagram showing an example of a feature point extraction result for a character string. In the case of a character string composed of a plurality of characters, a plurality of feature points having different coordinates depending on the character type are extracted.

  The feature amount calculation unit 222 uses the feature points input from the feature point calculation unit 221 to obtain invariants for processing such as rotation, enlargement, and reduction, and based on the invariants, feature amounts (for example, hash values) Is calculated. More specifically, the feature amount calculation unit 222 sets each feature point (that is, the coordinate value of the center of gravity of the connected region) input from the feature point calculation unit 221 as a feature point, for example, a distance from the feature point Four other feature points in the vicinity of small are extracted.

  FIG. 7 is an explanatory diagram showing a feature point of interest and surrounding feature points. As shown in the figure, with respect to the target feature point P1, for example, four feature points surrounded by the closed curve S1 are extracted in order of increasing distance from the target feature point P1. The feature point P2 is also extracted as one feature point). Also, for the feature point P2, for example, four feature points surrounded by the closed curve S2 are extracted in order of increasing distance from the feature point P2 as described above (for the feature point P2, the feature point is noticed). The feature point P1 is also extracted as one feature point).

  The feature amount calculation unit 222 selects three feature points from the four extracted feature points and calculates an invariant. Note that the number of feature points to be selected is not limited to three, and four, five, etc. feature points may be selected. The number of feature points to be selected differs depending on the type of invariant to be obtained. For example, an invariant obtained from three points is a similar invariant.

  FIG. 8 is an explanatory diagram showing an example of calculating an invariant using the feature point of interest P1, and FIG. 9 is an explanatory diagram showing an example of calculating an invariant using the feature point of interest P2. As shown in FIG. 8, three feature points are selected from four feature points around the target feature point P1, and each of the three invariants is set to H1j (j = 1, 2, 3). The invariant H1j is calculated by an expression represented by H1j = A1j / B1j. Here, each of A1j and B1j indicates the distance between feature points, and the distance between feature points is calculated based on the coordinate values of each peripheral feature point. Thereby, for example, even when the document is rotated, moved, or tilted, the invariant H1j does not change, and the similarity of images can be determined with high accuracy, and similar documents can be determined with high accuracy. Can do.

  Similarly, as shown in FIG. 9, three feature points are selected from the four feature points around the target feature point P2, and each of the three invariants is set to H2j (j = 1, 2, 3). The invariant H2j is calculated by an expression represented by H2j = A2j / B2j. Here, each of A2j and B2j indicates the distance between the feature points, and the distance between the feature points is calculated based on the coordinate value of each peripheral feature point as described above. Similarly, invariants can be calculated for other attention feature points.

The feature amount calculation unit 222 calculates a hash value (feature amount) Hi based on the invariant calculated by each feature point of interest. The hash value Hi of the feature point Pi of interest is represented by Hi = (Hi1 × 10 ² + Hi2 × 10 ¹ + Hi3 × 10 ⁰ ) / E. Here, E is a constant determined by how much the remainder is set. For example, when “10” is set, the remainder is “0” to “9”, and the hash value calculated by this can be taken. It becomes a range. Moreover, i is a natural number and represents the number of feature points.

  FIG. 10 is an explanatory diagram illustrating another example of invariant calculation based on the target feature point P3, and FIG. 11 is an explanatory diagram illustrating another example of calculation of the invariant based on the target feature point P4. As shown in FIG. 10, four combinations are selected from the four peripheral feature points P1, P2, P4, and P5 of the target feature point P3, and the invariant H3j (j = 1, 2, 3, 4) is described above. As in the case of, calculation may be performed by H3j = A3j / B3j.

Further, as shown in FIG. 11, four combinations are selected from the four peripheral feature points P2, P3, P5, and P6 of the target feature point P4, and an invariant H4j (j = 1, 2, 3, 4) is selected. May be calculated by H4j = A4j / B4j in the same manner as described above. 10 and 11, the hash value Hi of the target feature point Pi can be calculated by Hi = (Hi1 × 10 ³ + Hi2 × 10 ² + Hi3 × 10 ¹ + Hi4 × 10 ⁰ ) / E.

  The hash value as the feature amount is an example, and the hash value is not limited to this, and other hash functions can be used. In the above, an example in which four points are extracted as other feature points in the vicinity is shown, but the present invention is not limited to four points. For example, six points may be extracted. In this case, 5 points are extracted from 6 feature points, and for each of the 6 methods of extracting 5 points, 3 points are extracted from 5 points to obtain an invariant, and a hash value is calculated. good. Thus, one hash value can be calculated from one feature point, and a plurality of hash values can be calculated from one feature point.

  The feature amount calculation unit 222 stores an index representing a feature point of the document image, a hash value calculated based on the feature point, and coordinates of the feature point on the document image as a feature point coordinate table of the document image. It memorize | stores in 226.

  When registering a document image, for example, a document registration mode is selected from the operation panel 4 of the image forming apparatus. The control unit 227 determines whether or not the document registration mode is selected. When the document registration mode is not selected, normal copying, filing, e-mail transmission operation, and the like are performed. The document registration process is not only configured by a dedicated hardware circuit such as the document collation processing unit 24, but a computer program that defines the procedure of the document registration process is stored in a personal computer having a CPU, a RAM, a ROM, and the like. It can also be performed by causing a CPU to execute a computer program by loading.

  FIG. 12 is an explanatory diagram showing the structure of the feature point coordinate table 2261 of the document image. The feature point coordinate table 2261 of the document image includes columns for indexes representing feature points of the document image, hash values calculated based on the feature points, and coordinates (x ′ coordinate, y ′ coordinate) of the feature points on the document image. It is comprised by.

  As shown in FIG. 12, for example, the hash value calculated based on the feature point whose index representing the feature point of the document image is p1 is H1, and the coordinates of the feature point are (x11 ′, y11 ′). Similarly, the hash value calculated based on the feature point whose index representing the feature point of the original image is p2 is H8, and the coordinates of the feature point are (x32 ′, y32 ′).

  FIG. 13 is an explanatory diagram showing the structure of the hash table 2262 in the registration format. As shown in FIG. 13A, the structure of the hash table 2262 is composed of columns of hash values and indexes representing documents (registered formats). More specifically, a point index indicating the position in the document and an invariant (both not shown) are registered corresponding to the index indicating the document. The hash table 2262 is stored in the memory 226. In addition, a registration format (image / document image to be collated) is stored in the memory 226 in advance in order to determine the similarity of images. As shown in FIG. 13B, when the hash values are equal (H1 = H5), two entries of the hash table 2262 can be combined into one.

  FIG. 14 is an explanatory diagram showing the structure of the feature point coordinate table 2263 of the registration format. The registered format feature point coordinate table 2263 is stored in the memory 226, and an index representing a document for identifying each registered format, and an index representing a feature point for identifying a feature point calculated in each registered format. , Each feature point is composed of columns of coordinates (x coordinate, y coordinate) on the registration format.

  As shown in FIG. 14, for example, a registration format whose index representing a document is ID1 has feature points f11, f12, f13, f14,..., And the coordinates of the feature points on each registration format are (x11, y11), (x12, y12), (x13, y13), (x14, y14),. The same applies to the other registration format IDs 2.

  The voting processing unit 223 searches the hash table 2262 of the registered format stored in the memory 226 based on the hash value (feature amount) calculated by the feature amount calculation unit 222, and if the hash values match, the hash value Vote for an index representing a document registered in (i.e., an image for which a matching hash value has been calculated). The result of cumulative addition of votes is output to the similarity determination processing unit 224 as the number of votes.

  In addition, when the hash value of the document image and the hash value of the registration format match, the voting processing unit 223 determines which feature point of which registration format the feature point of the document image has voted, and determines the determination result. The feature point voting table 2264 is stored in the memory 226.

  FIG. 15 is an explanatory diagram showing the structure of the feature point voting table 2264. The feature point voting table 2264 includes indexes f11, f21,..., Fn1 (hereinafter referred to as “fn1”) representing the feature points of the registration format corresponding to the indexes ID1, ID2, ID3, ID4,. .., F12, f22,..., Fn2 (hereinafter referred to as “fn2”), and similarly, the feature points of the original image are provided in each element of the matrix composed of fn3, fn4, fn5, fn6, fn7,. Indexes p1, p2,... Represented are recorded.

  As shown in FIG. 15, for example, it is determined that the hash value calculated for the feature point of the document image represented by the index p1 matches the hash value of the feature point f11 of the index ID1 representing the document in the registered format. ing. Further, it is determined that the hash value calculated for the feature point of the document image represented by the index p2 matches the hash value of the feature point f32 of the index ID3 representing the document in the registered format. Furthermore, it is determined that the hash value calculated for the feature point of the document image represented by the index p3 matches the hash value of the feature point f13 of the index ID1 representing the document in the registered format. The same applies to the indexes p4, p5, p6, p7,.

  The similarity determination processing unit 224 determines, based on the voting result input from the voting processing unit 223, which registration format the read document image is similar to, and outputs the determination result to the writing determination processing unit 225. To do. More specifically, the similarity determination processing unit 224 calculates the number of votes input from the voting processing unit 223 by the maximum number of votes of the document image (the number of feature points × the number of hash values calculated from one feature point). The similarity is calculated by dividing by (represented). The similarity determination processing unit 224 compares the calculated similarity with a predetermined threshold Th (for example, 0.8), and if the similarity is equal to or greater than the threshold Th, the registration for which the similarity is calculated When it is determined that the format is similar and the similarity is smaller than the threshold Th, it is determined that there is no registered format similar to the document image, and the determination result is output to the writing determination processing unit 225. The number of votes obtained from the voting processing unit 223 is compared with a predetermined threshold. If the number of votes is equal to or greater than the threshold, it is determined that the document image is similar to a registered format registered in advance, and is further determined to be similar. It can also be determined that the registered format with the highest number of votes matches the original image.

  FIG. 16 is an explanatory diagram illustrating an example of similarity determination based on a vote result. As shown in FIG. 16, as a result of searching and voting the hash table 2262 for each hash value calculated from the read document image, the index representing the document is voted for the registration formats indicated by ID1, ID2, and ID3. Suppose. The normalized similarities N1, N2, and N3 are calculated by dividing the number of votes obtained as a result of the voting by the maximum number of votes of the document image. The calculated similarity is equal to or greater than the threshold Th because the index representing the document is in the registration format indicated by ID1, and thus the document image is determined to be similar to the registration format of index ID1 representing the document.

  When the determination result that the document image is similar to the registered format is output from the similarity determination processing unit 224, the writing determination processing unit 225 determines whether or not the document image has been written, and indicates whether or not writing has been performed. Outputs a judgment signal. More specifically, the writing determination processing unit 225 performs alignment processing for aligning the document image and the registration format, and calculates a difference between pixel values (for example, luminance values) of each pixel of the document image and the registration format. Difference processing to be calculated, determination processing for determining whether or not the document image has been written based on the calculated difference, and the like are performed.

  Next, an alignment process for aligning images by converting the coordinate system of the registered format into the coordinate system of the document image will be described. When the registration format coordinate system is converted to the original image coordinate system and image alignment is performed, the entire registration format can be coordinate-converted, or the coordinate conversion location can be set for each entry column of the registration format. However, it is also possible to convert the coordinates of only a part of the set points (or to be determined in advance). First, a case where the entire registration format is coordinate-converted and aligned with the document image will be described. In the following description, it is assumed that the document image is determined to have an index representing the document similar to the registration format of ID1.

  The writing determination processing unit 225 accesses the memory 226, and extracts the feature points of the document image in which the index representing the document is voted ID1 from the feature point voting table 2264. In the example of FIG. 15, the indexes representing the feature points of the document image are p1, p3, p4, and p7.

  The writing determination processing unit 225 identifies indexes f11, f13, f14, and f17 representing the feature points of the registered format corresponding to the indexes p1, p3, p4, and p7 representing the feature points of the extracted document image (hash values match). To do. The writing determination processing unit 225 refers to the feature point coordinate table 2263 of the registration format and the feature point coordinate table 2261 of the original image, and the matrix Jin and the formula ( A matrix Jout is obtained for the coordinates of the feature points of the original image shown in 2), and the conversion coefficient of the conversion matrix W is assumed to be expression (3).

The write determination processing unit 225 calculates the conversion matrix W by Expression (4). In this case, both sides of Equation (4) are multiplied by Jin transpose matrix Jin ^T to obtain Equation (5), and both sides of Equation (5) are multiplied by the inverse matrix of Jin ^T Jin to obtain Equation (6). To calculate a conversion coefficient. Using the conversion matrix W indicated by the calculated conversion coefficient, the writing determination processing unit 225 converts arbitrary coordinates (x, y) on the registration format into coordinates (x ', Y').

  FIG. 17 is an explanatory diagram showing the alignment of the document image and the registered format. As shown in FIG. 17, the registration format origin point (upper left pixel of the image) is obtained by associating the registration format feature points f11, f13, f14, and f17 with the feature points p1, p3, p4, and p7 of the document image. Even if the position of the upper left pixel of the original image is shifted, the origins of both images can be made coincident, and the registration format and the original image can be aligned. Note that the number of feature points in each image is not limited to four. Depending on the format of the registration format, when there are a large number of feature points, some feature points can be selected from them.

  Next, difference processing for calculating a difference between pixel values of each pixel of the original image and the registration format will be described. The writing determination processing unit 225 sequentially scans the pixels of the registered original image and the registration format, for example, from the upper left to the lower right of both images, so that the luminance value of the target pixel corresponding to both images is obtained. The difference is calculated. In this case, in consideration of the reproducibility of the pixel value when reading the document, when the image is represented by 256 gradations, the difference between the luminance values of the corresponding pixels of both images is about 5-10. If the position is within the range (difference threshold Tg), it is determined that the target pixel is the same. For example, if the luminance value of a pixel of interest in a document image is 210 and the luminance value of the pixel of interest corresponding to the registration format is 215, it is determined that both pixels of interest are the same. For example, if the luminance value of a pixel of interest in the original image is 210 and the luminance value of the pixel of interest corresponding to the registration format is 235, it is determined that the pixels of interest are not the same.

  The writing determination processing unit 225 determines whether or not a ratio obtained by dividing the number of pixels determined to be the same (determination pixel number M) by the number of pixels included in the registration format is smaller than a ratio threshold Ts (for example, 0.99). If it is determined and the calculated ratio is smaller than the ratio threshold value Ts, it is determined that there is writing in the document image, and a determination signal is output. If the calculated ratio is greater than or equal to the ratio threshold Ts, it is determined that there is no writing on the document image, and a determination signal is output. Note that it is determined whether the ratio obtained by dividing the number of pixels determined not to be the same by the number of pixels in the registration format is greater than a ratio threshold Td (for example, 0.01), and the calculated ratio is greater than the ratio threshold Td. If it is larger, it can be determined that the document image has been written, and if the calculated ratio is equal to or less than the ratio threshold Td, it can also be determined that there is no writing in the document image.

  That is, the writing determination process calculates a difference between pixel values of the acquired document image and the registration format (registered image), compares the calculated difference value with a predetermined threshold value (difference threshold value), and the pixel values are substantially the same. The number of pixels (determination pixel number) is calculated, a ratio obtained by dividing the calculated determination pixel number by the total number of pixels included in the registration format is calculated, and the calculated ratio is set as a predetermined threshold (ratio threshold). Comparison (large / small comparison) is performed, and whether or not writing is performed is determined according to the comparison result.

  Next, a description will be given of the case where the registration format and the document image are aligned by setting the location where coordinate conversion is performed for each registration format description column and converting the coordinates of only a part of the set location. In this case, it is determined whether or not writing is performed at a location set as a location where coordinate conversion is performed.

  FIG. 18 is an explanatory diagram showing an example of a registration format. As shown in FIG. 18A, the registration format (that is, a document in a predetermined format) is provided with columns for describing date, name, address, telephone number, and entry. Further, as shown in FIG. 18 (b), it is possible to set the coordinate conversion only for the name, address, and entry items (the part with a pattern in the figure) in each entry column of the registration format. .

  The location where the coordinate conversion is performed may be set in advance for each registration format, and the setting information (coordinate conversion target table of the registration format) may be stored in the memory 226. Alternatively, when the user operates the operation panel 4, For example, it can be configured so that it can be set for each description column such as name, address, and entry.

  FIG. 19 is an explanatory diagram showing an example of the coordinate conversion target table 2265 in the registration format. As shown in FIG. 19A, the coordinate conversion target table 2265 includes an index representing a document in a registration format, a description column of the registration format, and coordinates in a description column. Further, as shown in FIG. The positions on the registration format of the description column are specified by the coordinates on the registration format at the four corners of the description column. In FIG. 19B, as an example, the coordinates (x11, y11), (x12, y11), (x11, y12), (x12, y12) of the name column in the registration format description column whose index representing the document is ID1. ).

  As shown in FIG. 19, for example, in the registration format in which the index representing the manuscript is ID1, each description column of name, address, and entry is subject to coordinate conversion, and the name description column is on the coordinates of the registration format. It is specified by diagonal coordinate values (x11, y11), (x12, y12).

  In this case, the writing determination processing unit 225 accesses the memory 226 and extracts the feature point of the document image in which the index representing the document is voted ID1 from the feature point voting table 2264. The writing determination processing unit 225 identifies an index that represents a feature point of the registered format that corresponds to the index that represents the feature point of the extracted document image (has the same hash value). The writing determination processing unit 225 refers to the feature point coordinate table 2263 of the registration format, the feature point coordinate table 2261 of the document image, and the coordinate conversion target table 2265, and based on the feature points in the coordinate conversion target, as described above. Thus, the coordinates (x, y) in the description column on the registration format are converted into the coordinates (x ′, y ′) on the document image. Further, the writing determination processing unit 225 performs difference processing for calculating a difference between pixel values (for example, luminance values) of each pixel of the original image in the description column and the registration format, and writes the original image based on the calculated difference. Judgment processing is performed to determine whether or not.

  As a result, it is possible to perform the determination process by converting only the portion for determining the presence / absence of writing, and the processing effort can be reduced as compared with the case where the entire image is processed. Can be realized.

  Next, the operation of the image forming apparatus 100 will be described. FIG. 20 is a flowchart showing the procedure of document collation processing. The document collation process is not only configured by a dedicated hardware circuit, but a computer program that defines the procedure of the document collation process is loaded into a personal computer equipped with a CPU, RAM, ROM, etc., and the computer program is executed by the CPU. It can also be performed by executing. In addition to the document matching process, an output control process for controlling output such as copying or electronic distribution according to the result determined in the document matching process can be added to the computer program.

  The control unit 227 determines whether or not there is an operation by the user (document reading operation) (S11). If there is no operation (NO in S11), the control unit 227 continues the process of step S11 and waits for an operation by the user. When there is an operation by the user (YES in S11), the control unit 227 acquires a document image obtained from the document read by the color image input device 1 (S12).

  The control unit 227 calculates feature points based on the acquired document image (S13), and calculates a hash value (feature amount) based on the calculated feature points (S14). The control unit 227 searches the hash table 2262 of the registered format based on the calculated hash value (feature value), and votes for an index representing a document with a matching hash value (S15). In this case, the control unit 227 determines which feature point of which registration format the feature point of the document image has voted, and stores the determination result.

  The control unit 227 calculates the similarity by dividing the number of votes by the maximum number of votes of the document image (S16), and determines whether the calculated similarity is greater than the threshold Th (S17). When the similarity is larger than the threshold Th (YES in S17), the control unit 227 performs a write determination process (S18). Details of the write determination process will be described later. The control unit 227 outputs the determination result obtained by the write determination process (S19). Note that a CPU (not shown) in the color image processing apparatus 2 performs predetermined output control described later according to the determination result.

  The control unit 227 determines whether or not all document images have been acquired (S20). If all document images have not been acquired (NO in S20), the processing from step S12 is continued. On the other hand, when the similarity is not greater than the threshold value Th (NO in S17), the control unit 227 continues the processing from step S20. When all the document images have been acquired (YES in S20), the control unit 227 ends the process.

  FIG. 21 is a flowchart showing the procedure of the write determination process. The control unit 227 converts the coordinate system (coordinates) of the registration format determined to be similar to the document image into the coordinate system (coordinates) of the document image (S101). In this case, the entire registration format may be coordinate-converted, or only the description column in which the registration format is set may be coordinate-converted.

  The control unit 227 sets the determination pixel number M indicating the number of pixels having the same pixel value (for example, luminance value) among the pixels of the document image and the registration format to “0” (S102), and pays attention to the document image and the registration format. By scanning the pixels, the difference between the pixel values of each target pixel is calculated (S103).

  The control unit 227 determines whether or not the calculated difference is smaller than the difference threshold Tg (S104). When the difference is smaller than the difference threshold Tg (YES in S104), “1” is added to the determination pixel number M ( S105). The control unit 227 determines whether or not the processing has been completed for all the pixels of the original image and the registration format (S106). If the processing has not been completed for all the pixels (NO in S106), the control unit 227 and subsequent steps. Continue processing.

  When the difference is not smaller than the difference threshold value Tg (NO in S104), the control unit 227 continues the process from step S106. When the processing has been completed for all the pixels (YES in S106), the control unit 227 determines whether or not the ratio represented by (the number of determination pixels M / the number of pixels in the registered format) is smaller than the ratio threshold Ts ( S107).

  If the ratio is smaller than the ratio threshold Ts (YES in S107), the control unit 227 determines that there is writing (S108), and ends the process. On the other hand, when the ratio is not smaller than the ratio threshold Ts (NO in S107), the control unit 227 determines that there is no writing (S109), and ends the process.

  As described above, when performing the writing determination process, by determining the ratio of the number of pixels having the same pixel value in the original image and the registration format to the number of pixels in the registration format, the pixels on the original image are inherently caused by noise. Even when the pixel value is different from the pixel value, the effect of noise can be reduced by dividing by the number of pixels in the registered format.

  A CPU (not shown) of the color image processing apparatus 2 controls output processing such as copying, electronic distribution, and recording for the read document according to the determination result of the writing determination processing. When there are a plurality of documents, when it is determined that there is at least one document to be written, output processing such as copying, electronic distribution, and recording for the read document may be controlled.

  FIG. 22 is an explanatory diagram showing an example of control of output processing. In the figure, the selection option can be set in the apparatus in advance, or the user may set it on the operation panel 4. The selection option can be set by combining any one or a plurality of options. For example, when option F1 is set, copying or electronic distribution of an original image is prohibited when it is determined that there is writing in the original image, and copying or electronic distribution of the original image is prohibited when it is determined that there is no writing. Allowed.

  If option F2 is set, filing of the document image is permitted when it is determined that the document image is written, and filing of the document image is prohibited when it is determined that there is no writing. When option F3 is set, copying of registered format or electronic distribution is permitted when it is determined that the document image is written. Further, when option F4 is set, if it is determined that the document image is written, copying or electronic distribution is permitted only for writing in the registered format and insignificant portions (description column). That is, when the option F4 is set, copying or electronic distribution of important portions (specifically, portions where important matters, personal information, confidential information, etc. are written) in the document image is prohibited.

  FIG. 23 is an explanatory view showing an output example of a document image. In FIG. 23, as shown in the example of FIG. 18, the presence / absence of writing is determined in each of the description columns of the registration format, and in each of the description columns of the name, address, and entry, writing is performed in the other description fields. The presence or absence of is not determined. Further, it is assumed that the option F4 is set as output control. In this case, as shown in FIG. 23A, when the date, name, address, telephone number, and entry of the original image are written, the output image obtained by copying or electronically distributing the original image is As shown in FIG. 23 (b), the written columns of the name, address, and entry items are masked and not output because they are important parts. On the other hand, the date and telephone number entry fields are not important parts and are output together with the registration format (blank format without writing).

Embodiment 2
The original paper to be read is not limited to white paper, and recycled paper or lightly colored paper may be used. Even in such a case, it is possible to accurately determine the presence or absence of writing by performing the background removal processing of the document image. In this case, the document matching processing unit 22 can perform background removal processing. Alternatively, an input tone correction unit 23 is provided after the shading correction unit 21 and a document matching processing unit 22 is provided after the input tone correction unit 23 so that the input tone correction unit 23 performs background removal processing. You can also When performing background removal on a document image, the registration format is also registered by performing background removal processing. Alternatively, the background removal is performed on the document image so that the background has the same level as the background of the registered format. This is possible by appropriately setting a density correction table for performing background removal.

  The document collation processing unit 22 extracts a signal obtained by correcting and inverting the G signal from the RGB signals input from the shading correction unit 21, and for example, divides the density of 256 levels into 16 sections and generates a density histogram of the document image. create.

  In addition, the maximum density value determined to be the background is stored in advance in the memory 226 as the first threshold value, and the number of pixels is determined to be the background, that is, the background. The minimum value of the number of pixels determined to be is stored in advance in the memory 226 as the second threshold value. Further, a density correction table for removing the background corresponding to the density classification determined to be the background is stored in the memory 226.

  The document matching processing unit 22 searches from the high density side of the created density histogram, and is in a density category equal to or lower than the first threshold value determined to be the background, and has a density of pixels equal to or higher than the second threshold value. A section is extracted as a base. The document matching processing unit 22 performs background removal processing using a density correction table corresponding to the density classification extracted as the background. Note that a luminance signal (255-Yj) can be used instead of the G signal. Here, Yj is represented by Yj = 0.30 × Rj + 0.59 × Gj + 0.11 × Bj, where Rj, Gj, and Bj are the color components of each pixel RGB.

  FIG. 24 is a flowchart illustrating the procedure of the write determination process according to the second embodiment. The control unit 227 converts the coordinate system (coordinates) of the registration format determined to be similar to the document image into the coordinate system (coordinates) of the document image (S121). In this case, as in the first embodiment, the entire registration format may be coordinate-converted, or only the description column in which the registration format is set may be coordinate-converted.

  The control unit 227 creates a density histogram of the original image based on the signal obtained by correcting and inverting the G signal extracted from the original image (S122), and extracts the background by searching from the higher density side of the created density histogram. (S123). The control unit 227 removes the background of the original image using the density correction table corresponding to the density classification extracted as the background (S124).

  The control unit 227 sets the determination pixel number M indicating the number of pixels having the same pixel value (for example, luminance value) among the pixels of the document image and the registration format to “0” (S125), and pays attention to the document image and the registration format. By scanning the pixels, the difference between the pixel values of each pixel of interest is calculated (S126).

  The control unit 227 determines whether or not the calculated difference is smaller than the difference threshold Tg (S127). If the difference is smaller than the difference threshold Tg (YES in S127), “1” is added to the determination pixel number M ( S128). The control unit 227 determines whether or not the processing has been completed for all the pixels of the original image and the registration format (S129). If the processing has not been completed for all the pixels (NO in S129), the control unit 227 and subsequent steps. Continue processing.

  When the difference is not smaller than the difference threshold value Tg (NO in S127), the control unit 227 continues the process after step S129. When the processing has been completed for all the pixels (YES in S129), the control unit 227 determines whether or not the ratio represented by (number of determination pixels M / number of pixels in the registered format) is smaller than the ratio threshold Ts ( S130).

  If the ratio is smaller than the ratio threshold Ts (YES in S130), the control unit 227 determines that there is writing (S131), and ends the process. On the other hand, when the ratio is not smaller than the ratio threshold Ts (NO in S130), the control unit 227 determines that there is no writing (S132), and ends the process.

  Thereby, even if the original document to be read is recycled paper or lightly colored paper, it is possible to accurately determine whether or not writing is performed.

Embodiment 3
In the first and second embodiments, when the presence / absence of writing is determined, the ratio represented by (number of determination pixels M / number of pixels of registered format) is used. The writing determination processing can be realized by calculating the number of pixels by the document matching processing unit 22 without being limited thereto.

  FIG. 25 is a flowchart illustrating the procedure of the write determination process according to the third embodiment. The control unit 227 converts the coordinate system (coordinates) of the registered format determined to be similar to the document image into the coordinate system (coordinates) of the document image (S141). In this case, the entire registration format may be coordinate-converted, or only the description column in which the registration format is set may be coordinate-converted.

  The control unit 227 binarizes the document image and the registered format according to a predetermined binarization threshold (for example, 128 when the number of gradations of the document image is 256) (S142). The control unit 227 calculates the pixel number K1 of the document image after binarization (S143), and calculates the pixel number K2 of the registration format after binarization (S144).

  The control unit 227 determines whether (K1-K2) is larger than the pixel number threshold (S145), and when (K1-K2) is larger than the pixel number threshold (YES in S145), it is determined that there is writing ( S146), the process ends. On the other hand, when (K1-K2) is not larger than the pixel number threshold value (NO in S145), the control unit 227 determines that there is no writing (S147) and ends the process. The pixel number threshold value may be set by obtaining a value that can appropriately determine the presence or absence of writing based on many image samples.

  With the configuration in which the number of pixels is calculated, it is possible to accurately determine whether or not writing is performed, even when there is a slight writing, regardless of the size of the area of the description column of the document.

  In the third embodiment described above, the number of pixels after binarization is calculated. However, instead of this, the number of black pixels in the document image can also be calculated. Also in this case, it is possible to accurately determine whether or not writing is performed even if there is a slight writing regardless of the size of the area of the description column of the document.

  Further, although the configuration is such that the number of pixels after binarization is calculated, instead of this, by comparing the number of determination pixels M calculated in Embodiments 1 and 2 with a predetermined threshold, for example, determination pixels When the number M is larger than a predetermined threshold, it is determined that there is a large number of pixels having the same pixel value in the original image and the registered format, and it is determined that there is no writing. It can also be determined that there is writing on the assumption that the number of pixels having the same pixel value is small in the original image and the registration format.

  That is, in this case, the writing determination process calculates a pixel value difference between the acquired document image and the registration format (registered image), and compares the calculated difference value with a predetermined threshold value (difference threshold value). The number of pixels having substantially the same number (determination pixel number) is calculated, the calculated determination pixel number is compared with a predetermined threshold value (large / small comparison), and the presence / absence of writing is determined according to the comparison result. .

  Furthermore, the process for performing the write determination based on the ratio illustrated in FIG. 24 and the process for performing the write determination by calculating the number of pixels illustrated in FIG. 25 may be used in combination. In this case, when the ratio represented by (number of determination pixels M / number of pixels in the registration format) is smaller than the ratio threshold Ts and (K1-K2) is larger than the pixel number threshold (or the number of determination pixels) If M is not greater than a predetermined threshold), it is determined that there is writing, the ratio represented by (determination pixel number M / registered format pixel number) is not smaller than the ratio threshold Ts, and (K1-K2) is When it is not larger than the pixel number threshold value (or when the determination pixel number M is larger than a predetermined threshold value), it is determined that there is no writing. If any one of the determination conditions is not satisfied, priority can be given to any determination result in accordance with the type of document.

  By using both the write determination based on the ratio and the write determination based on the number of pixels, the write determination accuracy can be further improved.

Embodiment 4
In each of the embodiments described above, the document image is compared with the pixel value of the registration format when determining the presence / absence of writing. However, the determination of the presence / absence of writing is not limited to this, and registration is performed. It is also possible to determine whether or not a document image has been written using only the document image without comparing with the pixel value of the format.

  FIG. 26 is a block diagram illustrating a configuration of the write determination processing unit 225 according to the fourth embodiment. The writing determination processing unit 225 includes a pixel value calculation unit 2251, a distribution calculation unit 2252, an edge pixel determination unit 2253, a determination unit 2254, and the like. The processing of each unit described above is controlled by the control unit 227. In addition, data obtained by the above-described units is stored in the memory 226. As in the first embodiment, the writing determination processing unit 225 performs alignment processing for aligning the document image and the registration format before performing determination processing for determining whether or not there is writing in the document image. Since this processing content is the same as that of the first embodiment, description thereof is omitted.

  The pixel value calculation unit 2251 applies to each pixel in a pixel block (for example, a first pixel block composed of 7 × 7 pixels) including a plurality of pixels including the target pixel of the input document image (image data). On the other hand, an average value of density values of each pixel in a pixel block (for example, a second pixel block including 7 × 7 pixels) including a plurality of pixels including the pixel is calculated, and the calculated average value is , The pixel value of the pixel. Thereby, the average value of each pixel (49 pixels in the above example) in the first pixel block including the target pixel can be obtained. Note that the pixel value to be calculated is not limited to the average value, and other values such as the center value of the density value of each pixel in the second pixel block can be obtained.

  The distribution calculation unit 2252 searches for an average value of each pixel in the first pixel block including a plurality of pixels including the target pixel, and calculates a maximum density difference in the first pixel block. Note that the value to be calculated is not limited to the maximum density difference, and a dispersion value or the like can be calculated as long as the distribution of pixel values in the first pixel block can be grasped.

  The edge pixel determination unit 2253 compares the maximum density difference calculated by the distribution calculation unit 2252 with a predetermined edge determination threshold (for example, 30), and if the maximum density difference is larger than the edge determination threshold, the first pixel It is determined that the pixel of interest in the block is an edge pixel, and 1 is added to the number of edge pixels to count the number of edge pixels.

  The pixel value calculation, distribution calculation, and edge pixel determination processes described above are repeated for all target pixels of the document image.

  The determination unit 2254 compares the number of edge pixels counted by the edge pixel determination unit 2253 with a predetermined first determination threshold (for example, 5000) for each document image, and when the number of edge pixels is larger than the first determination threshold, When it is determined that there is writing in the document image and the number of edge pixels is smaller than the first determination threshold, it is determined that there is no writing in the document image.

  If the document image is a color document, the writing determination processing unit 225 can perform the above-described processes for each RGB plane (for each color component). Further, when the maximum density difference of any one of the plurality of color components is larger than the edge determination threshold, it is not always necessary to remove the background by determining that the pixel of interest is an edge pixel. Note that each of the above-described processes may be performed on only some of the color components. In this case, the processing capacity can be reduced.

  FIG. 27 is a flowchart illustrating the procedure of the write determination process according to the fourth embodiment. The write determination process is configured not only by a dedicated hardware circuit as in the above-described embodiment, but also by loading a program code defining the procedure of the write determination process into a personal computer equipped with a CPU, RAM, ROM, etc. The program code can be executed by the CPU.

  The control unit 227 converts the coordinate system (coordinates) of the registration format determined to be similar to the document image into the coordinate system (coordinates) of the document image (S151). In this case, the entire registration format may be coordinate-converted, or only the predetermined description column of the registration format may be coordinate-converted.

  The control unit 227 searches the coordinate conversion target table 2265, acquires the item name of the registered format determined to be similar to the document image, and the coordinate value of each item name (S152), and writes based on the acquired coordinate value. An area for determining the presence or absence of the image is specified on the original image and the registered format (S153).

  The control unit 227 calculates the average value of each pixel in the pixel block (first pixel block) including the target pixel (S154), searches the calculated average value, and determines the maximum density difference of the first pixel block. Calculate (S155).

  The control unit 227 determines whether or not the calculated maximum density difference is larger than an edge determination threshold (for example, 30) (S156). If the maximum density difference is larger than the edge determination threshold (YES in S156), the target pixel is determined. Edge pixels are counted as being edge pixels (S157). In this case, each time it is determined that the pixel is an edge pixel, the edge pixel is counted by adding 1 to the number of edge pixels. When the maximum density difference is not larger than the edge determination threshold (NO in S156), the control unit 227 performs the process of step S158 described later without counting the number of edges.

  The control unit 227 determines whether or not processing of all pixels of the document image has been completed (S158). If processing of all pixels has not been completed (NO in S158), the control unit 227 moves to the next pixel of interest. The processing after step S154 is continued. When the processing of all the pixels is completed (YES in S158), that is, when the processing is completed for all the pixels in the document image, the control unit 227 determines that the counted number of edge pixels is the first determination threshold (for example, 5000). ) Is determined (S159).

  When the number of edge pixels is larger than the first determination threshold value (YES in S159), the control unit 227 determines that there is writing on the document image (S160), and ends the process. If the number of edge pixels is not greater than the first determination threshold value (NO in S159), the control unit 227 determines that there is no writing on the document image (S161) and ends the process.

  In the fourth embodiment, it is possible to determine whether or not writing is performed using only image data of a document image without using image data of a registered format. In addition, by calculating the average value of density values in a pixel block (first pixel block) composed of a plurality of pixels including the target pixel for each color component, an isolated point, the influence of noise at the time of reading, and Thus, it is possible to suppress the influence of dirt on the background portion of the document and dust adhering to the background portion of the document, and to improve the determination accuracy of the presence or absence of writing. Furthermore, if any of the plurality of color components has a maximum density difference equal to or greater than the threshold value, it is determined that the pixel of interest is an edge pixel.

Embodiment 5
In each of the above-described embodiments, the presence / absence of writing on a document image is determined. However, the present invention is not limited to a document image, and electronic data (data created by application software), It can also be applied to digitized data (data obtained by converting data read by a scanner into a predetermined file format such as JPEG or PDF).

  For example, data submitted in the form of electronic data or digitized data is stored in a server, and the present invention can be applied to such data as application software. The data is preferably stored for each electronic data and file format.

  For electronic data, several types of software can be used. For example, using RIP (raster image processor), PDL (page description language) is interpreted and converted into a raster image (RGB data). The method of the invention may be applied.

  For digitized data, for example, in the case of an encoded image format such as JPEG or GIF, color conversion such as once performing a decoding process and further converting a YCC signal to an RGB signal as necessary. What is necessary is just to apply the method of this invention after a process.

  In the case of the PDF format, when the image data is stored in an encoded image format such as JPEG, the decoding method is executed and converted into an RGB signal, and then the method of the present invention is applied. For the vector data portion such as, the method of the present invention may be applied after conversion to RGB image data via RIP or the like. In the case of the PDF format, information for each object (text, figure, photo, etc.) is held as a tag, and data can be converted with reference to this information. The object information includes, for example, a font, the number of points, a color, a display position, and the like in the case of text, and an encoding method, a size, a display position, and the like in the case of a photograph.

  FIG. 28 is a block diagram showing a configuration of a document reading apparatus 500 according to the present invention. As shown in the figure, the document reading device 500 includes a color image input device 1, an A / D conversion unit 20, a shading correction unit 21, a document matching processing unit 22, and the like. Since the color image input device 1, the A / D conversion unit 20, the shading correction unit 21, and the document collation processing unit 22 are the same as those of the image forming apparatus 100 described above, description thereof is omitted.

  As described above, according to the present invention, whether or not processing such as copying can be performed can be determined more accurately than in the past. It is also possible to prevent unauthorized copying or outflow of important matters, personal information, confidential information, etc. written on the manuscript. Further, it is possible to prevent unauthorized copying or outflow of confidential information or the like more finely. In addition, it is possible to easily obtain a document that has the same format as a document that has been written but has no writing. In particular, when a large number of different formats (formats) are registered, a required format can be easily selected, and user convenience is improved. Furthermore, when important matters, personal information, confidential information, etc. written on a manuscript are leaked, information can be easily tracked and verified.

  In the above-described embodiment, the registration format and the hash table 2262 are stored in the memory 226 in advance. However, the present invention is not limited to this, and is connected to the image forming apparatus 100 through a communication line (network). The registration format may be stored in the storage unit of the server device, and the hash table 2262 may be distributed and stored in the memory 226.

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

  In the above embodiment, the document collation processing unit 22 includes the memory 226 and the control unit 227. However, the present invention is not limited to this, and the memory 226 and the control unit 227 are included in the document collation processing unit 22. The structure provided outside may be sufficient.

  The present invention provides output control for document collation processing, copying, electronic distribution, filing, etc. on a computer-readable recording medium in which program code (execution format program, intermediate code program, source program) to be executed by a computer is recorded. It is also possible to record program codes for processing. As a result, it is possible to provide a portable recording medium on which a computer program for performing the above processing is recorded. The recording medium may be a non-illustrated memory, for example, a program medium such as a ROM because processing is performed by a microcomputer, and a program reading device as an external storage device (not illustrated) is provided, and the recording medium is stored therein. It may be a program medium that can be read by being inserted.

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

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

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

1 is a block diagram illustrating a configuration of an image forming apparatus including an image processing apparatus according to the present invention. It is a block diagram which shows the structure of a document collation process part. It is a block diagram which shows the structure of a feature point calculation part. It is explanatory drawing which shows the example of the filter coefficient of a filter process part. It is explanatory drawing which shows the example of the feature point of a connection area | region. It is explanatory drawing which shows the example of the extraction result of the feature point with respect to a character string. It is explanatory drawing which shows an attention feature point and the surrounding feature point. It is explanatory drawing which shows the example of calculation of the invariant by an attention feature point. It is explanatory drawing which shows the example of calculation of the invariant by an attention feature point. It is explanatory drawing which shows the other example of calculation of the invariant by an attention feature point. It is explanatory drawing which shows the other example of calculation of the invariant by an attention feature point. It is explanatory drawing which shows the structure of the feature point coordinate table of an original image. It is explanatory drawing which shows the structure of the hash table of a registration format. It is explanatory drawing which shows the structure of the feature point coordinate table of a registration format. It is explanatory drawing which shows the structure of a feature point vote table. It is explanatory drawing which shows an example of the similarity determination based on a vote result. It is explanatory drawing which shows the alignment of a manuscript image and a registration format. It is explanatory drawing which shows an example of a registration format. It is explanatory drawing which shows an example of the coordinate conversion object table of a registration format. It is a flowchart which shows the procedure of a document collation process. It is a flowchart which shows the procedure of a write determination process. It is explanatory drawing which shows the example of control of an output process. FIG. 6 is an explanatory diagram illustrating an output example of a document image. 10 is a flowchart illustrating a procedure of write determination processing according to the second embodiment. 10 is a flowchart illustrating a procedure of write determination processing according to the third embodiment. 10 is a block diagram illustrating a configuration of a write determination processing unit according to Embodiment 4. FIG. 15 is a flowchart illustrating a procedure of write determination processing according to the fourth embodiment. 1 is a block diagram showing a configuration of a document reading apparatus according to the present invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Color image input device 2 Color image processing device 3 Color image output device 22 Document collation process part 221 Feature point calculation part 222 Feature-value calculation part 223 Voting process part 224 Similarity determination process part 225 Write determination process part 2251 Pixel value calculation part 2252 Distribution Calculation Unit 2253 Edge Pixel Determination Unit 2254 Determination Unit 226 Memory 227 Control Unit 2210 Achromatic Processing Unit 2211 Resolution Conversion Unit 2212 Filter Processing Unit 2213 Binarization Processing Unit 2214 Center of Gravity Calculation Unit

Claims (19)

An image processing method for determining whether or not an acquired original image is written,
Determining whether the acquired document image is similar to a registered image stored in advance,
When it is determined that the document image is similar to the registered image, it is determined whether or not the acquired document image is written.
An image processing method comprising: controlling output processing for an acquired document image or registered image according to a determination result.   The image processing method according to claim 1, wherein when it is determined that writing has been performed, output of the acquired document image is prohibited.   When it is determined that writing has been performed, output of writing at a location corresponding to one or a plurality of locations defined for a registered image similar to the document image of the acquired document image is prohibited. The image processing method according to claim 1.   The image processing method according to claim 1, wherein when it is determined that writing has been performed, output of a registered image determined to be similar to the acquired document image is permitted.   5. The image processing method according to claim 1, wherein when it is determined that writing has been performed, the acquired document image or a portion where writing has been performed is recorded. 6.   The image processing method according to claim 1, wherein when it is determined that writing has not been performed, output of the obtained document image is permitted. An image processing apparatus that determines whether or not an acquired original image is written,
Means for determining whether the acquired document image is similar to a registered image stored in advance;
A determination means for determining whether or not the acquired document image is written when it is determined that the document image is similar to the registered image;
An image processing apparatus comprising: a control unit that controls output processing for an acquired document image or registered image according to a determination result of the determination unit. The control means includes
The image processing apparatus according to claim 7, wherein when the determination unit determines that writing has been performed, output of the acquired document image is prohibited. Means for predetermining one or more locations of the registered image;
The control means includes
When it is determined by the determination means that writing has been performed, output of writing of a portion corresponding to a portion determined for a registered image similar to the document image of the acquired document image is prohibited. The image processing apparatus according to claim 7. The control means includes
8. The image processing apparatus according to claim 7, wherein if the determination unit determines that writing has been performed, output of a registered image determined to be similar to the acquired document image is permitted.   11. The recording apparatus according to claim 7, further comprising: a recording unit configured to record the acquired original image or a portion where writing is performed when the determination unit determines that writing is performed. The image processing apparatus according to item. The control means includes
The image processing apparatus according to claim 7, wherein when the determination unit determines that writing has not been performed, output of the acquired document image is permitted. It is provided with a background removal means for removing the background from the acquired document image,
The determination means includes
13. The apparatus according to claim 7, wherein a determination is made as to whether or not writing has been performed based on a document image whose background has been removed by the background removal means. Image processing apparatus. Pixel value calculating means for calculating each pixel value of each pixel in the first pixel block including the target pixel of the acquired document image based on the pixel value of each pixel in the second pixel block including the pixel; ,
Edge determination means for determining whether or not the pixel of interest is an edge pixel according to a distribution of pixel values of each pixel in the first pixel block calculated by the pixel value calculation means;
The determination means includes
14. The apparatus according to claim 7, further comprising: determining whether or not the acquired document image is written based on the number of edge pixels determined by the edge determination unit. The image processing apparatus according to any one of the above.   The image processing apparatus according to claim 14, wherein when the obtained document image is a color image, each of the pixel value calculation unit and the edge determination unit performs processing for each color component. .   16. An image comprising: the image processing apparatus according to claim 7; and an image forming unit that forms an output image based on an image processed by the image processing apparatus. Forming equipment.   An original reading unit that reads an original image and the image processing apparatus according to any one of claims 7 to 15, wherein the image processing unit processes the original image read by the original reading unit. An original reading apparatus characterized by comprising: A computer program for causing a computer to determine whether or not an acquired original image is written,
Means for determining whether the computer image is similar to a registered image stored in advance;
A computer program for causing a computer to function as means for determining whether or not a document image determined to be similar has been written.   A computer-readable recording medium on which the computer program according to claim 18 is recorded.

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