JP4533422B2 - Image processing apparatus and method, and storage medium - Google Patents

Description

The present invention relates to an image processing apparatus and method and a storage medium, is rather more details to an image processing apparatus and method and a storage medium reading processing the document image.

  Conventionally, as a technique related to document image processing, a copying machine that optically inputs a document, prints and outputs the entire image, a document database system that optically inputs a document and stores the document image, and a document There are techniques such as a facsimile apparatus that optically inputs a document and outputs the document image via a network or a line, and an OCR that optically inputs a document, recognizes characters, and outputs a text code.

However, conventional technologies cannot adapt to digitization and networking. In other words, due to the fact that input devices and output devices are now connected via a network, and colorization of documents,
1) If the input document image is stored and distributed as it is, the amount of data increases.
2) If the document image is uniformly compressed, the image quality suitable for reuse cannot be maintained.
3) The output image quality deteriorates depending on the output device such as a monochrome printer or a color printer.
4) When only text is distributed using character recognition processing (OCR), there is a lack of information such as figures and photographs.
5) When there is a misrecognition by OCR, there is a lack of meaning.

  The problem has been closed up.

  The first invention has been made in view of such problems, and image processing that makes it possible to reduce the amount of data while maintaining the document layout and to suppress deterioration in image quality during reproduction and output. An apparatus, an image reproducing apparatus, a system, a method, and a storage medium are provided.

  The second invention is intended to provide an image processing apparatus, an image reproducing apparatus, a system, a method, and a storage medium excellent in security in addition to the first invention.

  In addition to the first invention, the third invention is to provide an image processing apparatus, an image reproducing apparatus, a system, a method, and a storage medium that can absorb a difference in natural language for text in an image. is there.

In order to solve the above problems, for example, an image processing apparatus that solves the problems of the first and second inventions has the following configuration. That is,
Input means for inputting multivalued image data of a document;
Binary image generation means for generating binary image data from the input multi-value image data;
A layout analysis unit that divides the generated binary image data into regions for each attribute, and generates layout information for each of the divided regions;
First partial image cutting means for cutting a partial image of a corresponding area from the binary image data for a text attribute area based on the layout information;
Based on the layout information, second partial image cutting means for cutting a partial image of a corresponding area from the multi-valued image data for a non-text attribute area;
Encryption target attribute setting means for setting an attribute to be encrypted based on an instruction from the operator;
Of the partial images cut by the first partial image cutting unit or the second partial image cutting unit, a partial image corresponding to the attribute area set as the encryption target by the encryption target attribute setting unit is encrypted. An encryption means for generating encrypted data;
The first partial image cutout corresponding to the encrypted data generated by the encryption means corresponding to the encryption target area and the attribute area not set as the encryption target by the encryption target attribute setting means Or a saving means for saving the partial image cut by the second partial image cutting means and the layout information.

  According to the present invention, it is possible to reduce the amount of data while maintaining the original layout, and to suppress deterioration in image quality when reproducing and outputting. Furthermore, it is possible to make it excellent in security.

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

[First Embodiment]
FIG. 1 is a diagram illustrating a configuration concept and a data flow in the first embodiment.

  In the figure, reference numeral 101 denotes an input document including a printed matter to be input and image data handled on a computer, 102 denotes an image input unit (for example, an image scanner) for inputting the document as image data, and 103 denotes from the image input unit. The input original image data, 104 is a binarization processing unit for generating a binary image from the input document image, 105 is the generated binary image data, 106 is a document based on the binary image 105, FIG. The layout analysis unit 107 outputs information divided into areas for each attribute such as text and table, that is, layout analysis data, and 107 is layout analysis data.

  A character recognition unit 108 recognizes characters in an arbitrary area of the binary image based on the layout analysis data and outputs code information or the like as character recognition data, 109 a character recognition data, and 110 a layout analysis data. A document image comprehension data storage unit 111 that stores image data and character recognition data as document image comprehension data while adaptively changing the storage conditions based on the input document original image and binary image, 111 is a document image comprehension Document image comprehension data output unit for outputting data, 112 is document image comprehension data, 113 is a document image comprehension data input unit for inputting document image comprehension data, and 114 is while adaptively changing the reproduction conditions from the document image comprehension data Document image understanding / reproducing unit for creating a reproduced document image, 115 a reproduced document image, and 116 an image output for outputting the reproduced document image , 117 is an output document.

  FIG. 2 shows an example of a system apparatus configuration representing the first embodiment.

  In the figure, 201 is a computer that performs document image understanding processing (for example, processing up to the document image understanding data output unit 111 in FIG. 1), 202 is a storage device that stores data, 203 is a scanner dedicated to image input, Reference numeral 204 denotes a color printer; 205, a facsimile; 206, a digital color copier (having functions as a color scanner and a color printer) that inputs and outputs color images in a composite manner; 207, a printer server that manages the printer; Is a monochrome printer, 209 is a file server that manages a database, 210 is a database, 211 is a digital copier that inputs and outputs monochrome images in combination, and 212 is a network that connects the digital devices. .

  Next, the processing flow will be described with reference to the flowcharts of FIGS. 3 to 10 and FIGS. 1, 2, and 14 to 17.

  In this embodiment, a document image is input as 24-bit (RGB each 8 bits) multi-value image data from a scanner (203 or 206), distributed over a network, and sent to a monochrome printer (208, 211, or 205) at a distribution destination. An image understanding processing system for output will be described.

  First, in step S301 in FIG. 3, the computer 201 inputs a document image as a multi-value image from the image input unit 102 (scanner 203 in FIG. 1).

  In step S302, the binarization processing unit 104 creates a binary image. The binary image creation method is performed by calculating a luminance component from the read image data RGB (8 bits each) and comparing the calculated luminance with a predetermined threshold value.

  In step S303, based on the binary image thus created, the layout analysis unit 106 divides the document image into regions for various attributes such as diagrams, texts, and tables, and outputs layout information (FIG. 15). In the layout information, as shown in FIG. 15A, a plurality of rectangular areas (partial images) are set for one page of document image, and data of the number of partial images is stored. Each partial image includes identification information (layout area ID), cutout width, height, image type, compression method, image size, and cutout image data. The image type includes identification information as to whether it is a binary image or a 24-bit multi-valued image as shown in FIG. 7B, and the compression method includes MMR, JPEG, etc. as shown in FIG. Of course, other types and numbers of image types and compression methods may be used.

  In step S304, character recognition (character recognition) processing is performed on a character image in an area including characters such as text and a table. In step S305, the document image understanding data storage unit (108) uses the layout analysis data as a basis. Then, the document image understanding data is created and stored while adaptively changing the storage conditions from the character recognition data and the multi-value image and binary image that are the input document original images.

  Here, a specific example of document image understanding data storage processing will be described with reference to the flowcharts of FIGS. 5, 6, 7, and 8.

  In this example, when the document image understanding data storage process (S305) is entered, layout analysis data of each divided area is first analyzed in step S501 to classify area attributes and document layout types of each area.

  An example of document type classification is shown in FIG.

  First, in step S601, the area (AS1) of the entire document is calculated.

Next, in step S602, the total area area (DS1) of the area attributes “text” and “table” is calculated. In step S603, the total area (DS1) is preliminarily calculated with respect to the entire document area (AS1). It is larger than the set ratio (TS1 [%]). That is, DS1> AS1 × TS1 / 100
If so, the document is determined to be a “text-based” layout document (step S604).

In step S603, if the text type is not determined, the total area area (PS1) of the area attributes “line figure” and “picture, photograph” is calculated (step S605), and is previously calculated in step S606. When the total area (DS1) of “text” and “table” is larger than the total area (PS1) of area attributes “line figure” and “picture, photograph”,
That is, DS1> PS1
If so, the document is determined to be a “text-based” layout document. (Step S604)
Otherwise, in step S607, the document is determined to be a “picture system” layout document.

  Returning to FIG. 5, when the document layout type is determined as described above, the process proceeds to step S502, and the process branches according to each layout type. If the document layout type is “picture type”, a picture type document image understanding data storage process is performed in step S503.

  The picture-based document image understanding data storage process will be described in more detail with reference to FIG.

  In the picture-based document image comprehension data saving process, first, the layout analysis data of each divided area is analyzed again in step S701. Next, if the area attribute is “text” in step S702, the entire cut-out destination is determined in step S703. A binary image is selected as an image, and a partial image is cut out using the coordinate data of the area of the layout analysis data in step S705.

  If the region attribute is not “text” in step S702, a multi-valued image that is the original image is selected as the entire cut-out image in step S704, and the coordinate data of the region of the layout analysis data is used in step S705. The multi-valued image that is the partial image is cut out.

  Next, the image type of the partial image cut out in step S706 is checked. If it is a binary image type, a binary image compression process is performed in step S707 (for example, MMR), and the compressed partial image is converted into a document in step S709. Storage processing is performed as image understanding data (FIG. 14).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S706 is a multi-value image type, multi-value image compression processing is performed in step S708 (for example, JPEG), and the compressed partial image is converted into document image understanding data in step S709. The storage process is performed as shown in FIG.

  As shown in FIG. 14, FIG. 15 and FIG. 16, the example of the detailed document image comprehension data of this example uses the appearance order in the layout analysis data of the target layout area data as the layout area data ID. The cut width, the cut height, the image type, the compression method, the image size, and the compressed cut image are stored as partial image information.

  Next, in step S710, it is checked whether or not there is a remaining divided area. If there is, the process returns to step S701, and the above processing is repeated for the remaining divided areas. The document image understanding data storage process is terminated.

  Returning to FIG. If the document layout type is “text type” in step S502 in the figure, the process proceeds to step S504, and text type document image understanding data storage processing is performed.

  Document image understanding storage processing when the document type is text-based will be described with reference to the flowchart of FIG. 8 and FIGS.

  In this example, when the document image understanding storage process (step S305 in FIG. 3) is entered, first, in step S801, the layout analysis data of each divided area is analyzed again to obtain the area attribute of each area.

  Next, if the area attribute is “text” in step S802, the character recognition data of the area is selected in step S803, and the character recognition data is stored as document image understanding data (FIG. 14) in step S804.

  As shown in FIG. 17, the more detailed example of the document image comprehension data of this example uses the appearance order in the layout analysis data of the target layout area data as the ID of the layout area data, and the number of lines and the line spacing. , Character codes from 1st to nth, character recognition distance from 1st to nth, character font type, character size, character position coordinates, uncertainty flag, etc. are stored as character recognition data .

  If the area attribute is not “text” in step S802, the original image is selected as the entire cut-out image in step S805, and the partial image is cut using the coordinate data of the area of the layout analysis data in step S806.

  In step S807, in this example, it is determined that the original image is a multi-valued image. Therefore, in step S808, compression processing for a multi-valued image is performed (for example, JPEG), and the compressed partial image is converted into a document in step S809. Storage processing is performed as image understanding data (FIG. 14).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  As shown in FIG. 16, the more detailed example of the document image understanding data of this example uses the appearance rank in the layout analysis data of the target layout area data as the layout area data ID, and the cut width and cut height. The image type, compression method, image size, and compressed cut image are stored as partial image information.

  Next, in step S810, it is checked whether or not there is a remaining divided area. If there is, the process returns to step S801, and the above processing is repeated for the remaining divided areas. If there is no remaining divided area, the document image understanding storage process is terminated.

  As described above, when the understanding data for each layout type for the document image is stored, the process proceeds to step S505 in FIG. 5 and the stored document image understanding data is output.

  Furthermore, in this embodiment, the document image understanding data created and stored in the document image understanding data storage unit (110) is output to the network by the document image understanding data output unit (111), and is distributed to the usage destination via the network. Is done.

  As shown in FIG. 4, the user (also a client) inputs document image understanding data by the document image understanding data input unit (113) in step S401, and adaptively reproduces the reproduction conditions from the document image understanding data in step S402. Create a replay document image while changing.

  A specific example of the document image understanding data reproduction process will be described with reference to FIG.

  First, in step S901, an image that is a background of a reproduced document image is created to be a white background.

  Next, in step S902, the document image understanding data is analyzed. In step S903, the partial region attribute is inspected. If the attribute is “text”, it is inspected in step S904 whether character recognition data exists. To do.

  If character recognition data exists, character recognition data (including a character code) is extracted from the document image comprehension data in step S905. In step S906, a character font is combined with the white base image from the extracted character recognition data. Create

  If the attribute of the partial area is non- “text” in the inspection in step S903 or no character recognition data exists in the inspection in step S904, the image data of the partial area is extracted from the document image understanding data in step S907. In step S908, a reproduced image is created by combining the extracted partial image and its coordinate data with a white background image.

  As an example of the reproduction image composition processing (step S908), as shown in FIG. 10, the image type of the partial image is extracted from the document image understanding data in step S1001, and the image type is “binary image type” in step S1002. If so, in step S1003, pseudo 24-bit conversion processing is performed to convert black and white pixels of the binary image into black and white pixels of a 24-bit multi-valued image, respectively.

  In this example, the black pixel of the binary image is 1, the white pixel is 0, the black pixel of the 24-bit multi-valued image is R = 0, G = 0, B = 0, and the white pixel is R = 255, G = 255, B = 255 (R is a Red component, G is a Green component, and B is a Blue component each having an 8-bit value).

  In step S1002, if the image type is “24-bit multi-value image type”, the partial image as it is is used for synthesis.

  Next, in step S1004, a logical operation is performed on each pixel of the partial image with respect to the base image and the partial image to create a composite image.

  In this example, a composite image of white pixels (R = 255, G = 255, B = 255) of the background image and black pixels (R = 0, G = 0, B = 0) of the partial image is a black pixel (R = 255). (0, G = 0, B = 0).

  When the reproduction synthesis processing (S908) is completed for one partial image by the above processing, it is checked in step S909 whether or not there is a remaining partial area, and if it exists, the process returns to step S902. The above process is repeated for the remaining partial areas. If there is no remaining partial area, the document image understanding data reproduction process is terminated.

  Returning to FIG. 4, when the reproduction process is performed as described above, the reproduction image is output as an output document from the monochrome printer which is the image output unit (116) in step S403.

[Second Embodiment]
In the document image understanding data storage process (step S305), even in a document whose area attribute is text as a result of the layout analysis, the image state is inadequate for the character recognition process (the presence of noise, a low resolution image, etc.). In some cases, it is possible to store reliable image data instead of uncertain character recognition data.

  Another example of specific document image understanding data storage processing will be described below with reference to the flowchart of FIG.

  In this example, when the document image understanding data storage process (step S305) is entered, the layout analysis data of each divided area is first analyzed in step S1101 to classify the area attributes and document layout type of each area (FIG. 6).

  If it is determined in step S1102 that the picture-type document layout is determined, a document image understanding data storage process (FIG. 7) is executed as a picture-type document layout in step S1106.

  If it is determined in step S1102 that the text-based document layout is determined, the overall character recognition reliability (ZNr) is calculated in step S1103.

As an example of the overall character recognition reliability, when the recognition distance value (D) that is the recognition calculation result for each character is less than a predetermined threshold (Td),
That is, D <Td
If it is, the uncertain flag of the character recognition data is made valid (= “1”) and the uncertain flag is not valid (= “0”). n) is obtained, and the ratio (n / N) to the total number of characters (N) is defined as the total character recognition reliability (ZNr).
That is, ZNr = n / N
Asking.

Next, in step S1103, the overall character recognition reliability (ZNr) is inspected, and the overall character recognition reliability is greater than a predetermined threshold (Tr).
That is, ZNr> Tr
If YES, the document image understanding data storage process (FIG. 8) is executed as a text document layout in step S1105.

Also,
ZNr ≦ Tr
If YES, the document image understanding data storage process (FIG. 7) is executed as a picture document layout in step S1106.

  As a result, if the quality of the original document is poor or the character quality printed on it is poor, the rate of misrecognition increases, so that the recognition result is not saved and the picture document can be made. It becomes possible. Accordingly, when reproducing on the client side, it is possible to reproduce the original document image faithfully.

[Third Embodiment]
In the document image understanding data storage process (step S305), even in a document whose area attribute is text in the layout analysis result, the state of the image for each partial area is inappropriate for the character recognition process (the presence of noise or low resolution images). If it is determined that the image is inappropriate, it is possible to store not only unreliable character recognition data but also reliable image data.

  A specific example of document image understanding data storage processing will be described with reference to the flowchart of FIG.

  In this example, when the document image understanding data storage process (step S305) is entered, the layout analysis data of each divided area is first analyzed as shown in FIG. 5 to classify the area attributes and document layout type of each area.

  Next, when it is determined that the layout is a text-based document layout, the layout analysis data is analyzed again in step S1201, and the partial region attribute is checked in step S1202. If the attribute is “text”, character recognition is performed in step S1203. Data is selected, character recognition data storage processing is performed in step S1204, and character recognition reliability (ZMr) of the partial area is calculated in step S1205.

As an example of the partial region character recognition reliability, when the recognition distance value (D) that is the recognition calculation result for each character is less than a predetermined threshold (Td),
That is, D <Td
In this case, the uncertain flag of the character recognition data is made valid (1) and the uncertain flag is not valid in the partial area (0). ) And the ratio (m / M) to the number of characters (M) in the region is used as the partial region character recognition reliability (ZMr).
That is, ZMr = m / M
Asking.

Next, in step S1206, the partial area character recognition reliability (ZMr) is inspected, and the partial area character recognition reliability is greater than a predetermined threshold (Tr).
That is, ZMr> Tr
If not, the image data storage process is not executed, and it is checked in step S1214 whether or not there is a remaining partial area. If there is, the process returns to step S1201, and the remaining partial areas are described above. Repeat the process.

If there is no remaining partial area, the document image understanding data storage process is terminated.
Also,
ZMr ≦ Tr
If not, it is determined as NG, and the image data storage processing in step S1207 and subsequent steps is executed.

[Fourth Embodiment]
It is also possible to adaptively select whether to synthesize a character font or to synthesize a region character image in accordance with the reliability of the character recognition data during the document image understanding reproduction process.

  The processing flow will be described below with reference to the flowcharts of FIGS.

  In this example, the character recognition reliability is obtained for each partial region, and whether to output in character font or region image is selected. Assume that the document image area data includes both character recognition data and image data.

  In this example, first, at the reproduction / use destination of the document image understanding data, the document image understanding data is input by the document image understanding data input unit (113) in step S401, and the reproduction condition is adaptively determined from the document image understanding data in step S402. Create a replay document image while changing.

  The document image understanding data reproduction process will be described with reference to the flowchart of FIG.

  In step S1301, an image serving as a background of the reproduced document image is created so as to be a white background.

  Next, the document image understanding data is analyzed in step S1302. If the attribute of the partial area is “text” in step S1303, the character recognition data is inspected in step S1304, and there is character recognition data. For example, character recognition data is extracted from the document image understanding data in step S1305, and the character recognition reliability is checked in step S1306.

  As an example of how to check the character recognition reliability in this example, the ratio of the uncertainty flag of the character recognition data in the partial area is used.

Specifically, the sum (m) of the number of characters (0) for which the uncertainty flag is not valid in the partial area is obtained, and the ratio (m / M) to the number of characters (M) in the area is determined as the partial area character recognition reliability. Degree (ZMr).
That is, ZMr = m / M
Asking.

Next, in step S1307, the partial area character recognition reliability (ZMr) is checked, and the partial area character recognition reliability is greater than a predetermined threshold (Tr2).
That is, ZMr> Tr2
In step S1308, a character font is combined with a white background image from the extracted character recognition data to create a reproduction image.

  If the character recognition reliability is equal to or lower than a predetermined threshold, partial image data is extracted from the document image understanding data, and a reproduced image is generated by combining the extracted partial image and its coordinate data with a white background image in step S1309. To do.

  In step S1303, partial image data is extracted from the document image understanding data even if the attribute of the partial area is non- "text". In step S1304, the extracted partial image and its coordinate data are combined with a white background image and reproduced. Create an image.

  When the reproduction synthesis process (step S1309) is completed for one partial image by the above processing, it is checked in step S1310 whether or not there is a remaining partial area, and if it exists, the process returns to step S1302. The above processing is repeated for the remaining partial areas.

  If there is no remaining partial area, the document image understanding data reproduction process is terminated.

  In step S403, the synthesized output image of one page is output as an output document from the image output unit (step 114).

As described above, according to the first to fourth embodiments,
1) Reducing the amount of data when saving document images
2) Reducing the load on network traffic when delivering document images,
3) Maintaining high image quality suitable for reuse during storage and distribution of document images,
4) Prevention of document image quality degradation and information loss during output,
5) Facilitating reuse of electronic documents such as DTP (desktop publishing)
This has the effect of peeling off.

[Fifth Embodiment]
In addition to the above embodiment, in the fifth embodiment, an example in which security is emphasized will be described.

  FIG. 18 is a diagram showing a configuration concept and a data flow in the fifth embodiment.

  In the figure, reference numeral 2101 denotes an input document including a printed matter to be input and image data handled on a computer, 2102 denotes an image input unit for inputting the document as an image, and 2103 denotes original image data input from the image input unit. Reference numeral 2104 denotes a binary image output unit that creates a binary image from the input document image, 2105 denotes the created binary image, and 2106 denotes a document based on the binary image for each attribute such as a figure, text, or table. Layout analysis unit that outputs information divided into areas, that is, layout analysis data, 2107 is layout analysis data, 2108 is based on layout analysis data, and adaptively changes storage conditions from the input document original image and binary image The document image understanding data storage unit 2109 stores the image data and the encrypted data as document image understanding data. Document image comprehension tag output unit for outputting solution data, 2110 is document image comprehension data, 2111 is a document image comprehension data input unit for inputting document image comprehension data, and 2112 is adapted to change reproduction conditions adaptively from the document image comprehension data. While, a document image comprehension / reproducing unit that creates a reproduced document image, 2113 is a reproduced document image, 2114 is an image output unit that outputs a reproduced document image, 2115 is an output document, and 2116 is a feature of the fifth embodiment. This is an encryption processing unit for encrypting character recognition data and image data in the area.

  The system in which the above data flow and processing are performed is the same as in FIG.

  Next, the flow of processing will be described with reference to the flowcharts of FIGS. 19, 20, and 21, and FIGS. 18, 2, 14, 15, 27, 28, and 29.

  Also in this embodiment, a 24-bit multi-valued image is input from a scanner (203 or 206), is automatically encrypted and stored in an area of “text” attribute, is distributed over a network, and is a monochrome printer at a distribution destination. An image understanding processing system when outputting to (208, 211, or 205) will be described.

  First, in step S2301, a document image is input as multi-value image data from a scanner which is an image input unit (2102). In step S2302, a binary image is generated by the binary image output unit (2104), and in step S2303, the layout analysis unit (2106) generates a document image for each attribute such as a figure, text, or table. The layout information is output by dividing the area. This layout information is the same as that in FIG. 15 in the first embodiment described above.

  Next, in step S2304, the document image understanding storage unit (2108) understands the document image while adaptively changing the storage conditions from the multi-value image and the binary image that are the input document original images based on the layout analysis data. Create and save data. At that time, encryption processing of data in a predetermined specific area is also performed.

  In the present embodiment, it is assumed that image data in the “text” area is encrypted.

  An example of document image understanding storage processing will be described with reference to the flowchart of FIG. 20 and FIGS.

  In this example, when the document image understanding storage process (step S2304) is entered, first, in step S2501, the layout analysis data of each divided area is analyzed to classify the area attributes of each area.

  Next, if the area attribute is “text” in step S2502, a binary image is selected as the cut-out whole image in step S2503, and a partial image is cut out using the coordinate data of the area of the layout analysis data in step S2504.

  In step S2505, the partial image is encrypted to create encrypted data. In step S506, the encrypted data is stored as document image understanding data.

  In a more detailed example of the document image understanding data of this example, as shown in FIG. 27, the appearance rank in the layout analysis data of the target layout area data is used as the ID of the layout area data, and the cut width and the cut height. The image type, the compression method, the image size, and the encrypted cut image are stored as the partial image encryption data. At this time, the encryption flag is enabled (= 1) to indicate that the data is encrypted.

  In step S2502, if the area attribute is non- "text", an original image is selected as the entire cut-out image in step S2507, and a partial image is cut using the coordinate data of the area of the layout analysis data in step S2508. .

  Next, the image type of the partial image cut out in step S2509 is checked, and if it is a binary image type, a binary image compression process is performed in step S2510 (for example, MMR), and the compressed partial image is converted into a document in step S2511. Storage processing is performed as image understanding data (FIG. 27).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S2509 is a multi-valued image type, multi-valued image compression processing is performed in step S2512 (for example, JPEG), and the compressed partial image is converted into document image understanding data in step S2511. The storage process is performed as shown in FIG.

  In the example of the document image understanding data of this example, as shown in FIG. 27, the appearance order in the layout analysis data of the target layout area data is taken as the ID of the layout area data, the cut width, the cut height, the image type The compression method, the image size, and the compressed cut image are stored as partial image data.

  Next, in step S2513, it is checked whether or not there is a remaining divided area. If it exists, the process returns to step S2501, and the above processing is repeated for the remaining divided areas.

  If there is no remaining divided area, the document image understanding data storage process is terminated. Then, the process proceeds to step S2305 in FIG. 19 to output document image understanding data.

  Here, in this embodiment, the document image understanding data created and stored in the document image understanding storage unit (2108) is output to the network by the document image understanding data output unit (2109) and distributed to the users via the network. Is done.

  At the user, processing is performed according to the flowchart of FIG. 4 as described in the first embodiment. That is, in step S401, the document image understanding data input unit (2111) inputs the document image understanding data, and in step S402, a reproduction document image is created while changing the reproduction condition adaptively from the document image understanding data.

  An example of specific document image understanding reproduction processing (step S402) in the fifth embodiment will be described with reference to the flowchart of FIG.

  In this example, first, in step S2601, an image that is a background of a reproduced document image is created so as to be a white background. Next, the document image understanding data is analyzed in step S2602. If the encryption flag in the partial area encrypted data is valid (= 1) in step S2603, the document image understanding data is encrypted in step S2604. Retrieve data, decrypt, and return to partial image.

  If the encryption flag is not valid, the image data is extracted from the document image understanding data in step S2605. In step S2606, a reproduced image is created by combining the extracted partial image and its coordinate data with a white background image.

  An example of the reproduction synthesis process (step S2606) follows the flowchart of FIG. 10 described in the first embodiment.

  That is, in step S1001, the image type of the partial image is extracted from the document image understanding data. If the image type is “binary image type” in step 1002, the black and white pixels of the binary image are respectively determined in step S1003. A pseudo 24-bit conversion process is performed to convert the 24-bit multi-valued image into black and white pixels.

  In this example, the black pixel of the binary image is 1, the white pixel is 0, the black pixel of the 24-bit multi-valued image is R = 0, G = 0, and B = 0, and the white pixel is R = 255, G = 255, B = 255 (R is a Red component, G is a Green component, and B is a Blue component each having an 8-bit value).

  In step S1002, if the image type is “24-bit multi-value image type”, the partial image as it is is used for synthesis. Next, in step S1004, a logical operation is performed on each pixel of the partial image with respect to the base image and the partial image to create a composite image.

  In this example, the composite image of the white pixels (R = 255, G = 255, B = 255) of the background image and the black image (R = 0, G = 0, B = 0) of the partial image is a black pixel (R = 0, G = 0, B = 0).

  When the reproduction / combination processing (S2606) is completed for one partial image by the above processing, it is checked in step S2607 whether the remaining partial images exist. If there is, the process returns to step S2602. The above process is repeated for the remaining partial areas. If there is no remaining partial area, the document image understanding data reproduction process is terminated.

  In step S403 in FIG. 4, the reproduction image is output as an output document from the monochrome printer as the image output unit (2114).

[Sixth Embodiment]
In the fifth embodiment, the text area is encrypted, but it is also possible to encrypt only the specific attribute selected by the operator. This example will be described as a sixth embodiment.

  A specific example will be described with reference to FIG. 19 and with reference to FIGS. FIG. 23 is also a modification of FIG.

  First, in step S2301, a document image is input as a multivalued image from a scanner which is an image input unit (2802).

  Next, the operator instruction section (2816) instructs the area attribute to be encrypted. For example, the input image is displayed, and an instruction is given to encrypt the “table” attribute area in the image.

  Next, in step S302, a binary image is generated by the binary image output unit (2804), and in step S2303, the layout analysis unit (2806) converts the document image into various forms such as figures, texts, and tables. The layout information is divided into areas for each attribute and layout information (FIG. 15) is output.

  In step S2305, the document image understanding storage unit (2808) adaptively changes the storage conditions from the multi-value image and the binary image that are the input document original images based on the layout analysis data and the operator's instruction. While creating and saving document image understanding data.

  A more specific example of document image understanding storage processing will be described with reference to the flowchart of FIG. 23 and FIGS.

  In this example, after entering the document image understanding storage process (step S2305), first, the attribute set by the operator is set in step S2901. FIG. 30 shows an example of the display screen at this time. An encryption designation combo box at the upper right of the window is designated, and encryption is designated for an appropriate area attribute. Here, it is assumed that the operator indicates “text”. Setting may be performed by designating a pointing device or the like in the area.

  In step S2902, the layout analysis data of each divided area is analyzed to classify the area attributes of each area. If the region attribute is designated as the encryption target in step S2903, that is, in the case of the above example, “text”, a binary image is selected as the entire cut-out image in step S2904. In step S2905, the partial image is cut out using the coordinate data of the layout analysis data area.

  In step S2906, the partial image is encrypted to create encrypted data. In step S2907, the encrypted data is stored as document image understanding data.

  At this time, the encryption flag is enabled (= 1) to indicate that the data is encrypted.

  In step S2903, if the area attribute indicates a non-encrypted area, the original image is selected as the entire cut-out image in step S2908, and the partial image is selected using the coordinate data of the area of the layout analysis data in step S2909. cut out.

  Next, the image type of the partial image cut out in step S2910 is checked. If the image type is a binary image type, binary image compression processing is performed in step S2911 (for example, MMR), and the compressed partial image is converted into a document in step S2913. Storage processing is performed as image understanding data (FIG. 27).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S2910 is a multi-value image type, multi-value image compression processing is performed in step S2912 (for example, JPEG), and the compressed partial image is converted into document image understanding data in step S2913. The storage process is performed as shown in FIG.

  As shown in FIG. 27, the more detailed example of the document image understanding data of the present example uses the appearance rank in the layout analysis data of the target layout area data as the layout area data ID, and the cut width and cut height. The image type, compression method, image size, and compressed cut image are stored as partial image data.

  Next, in step S2914, it is checked whether or not there is a remaining divided area. If it exists, the process returns to step S2902, and the above processing is repeated for the remaining divided areas. If there is no remaining divided area, the document image understanding data storage process is terminated. In this way, document image understanding data is output in step S2305.

[Seventh Embodiment]
In the sixth embodiment, encryption is not set for each attribute. However, the operator checks the input image and layout analysis result, and selects a desired region from the analysis result. It is also possible to instruct encryption.

  A specific example will be described with reference to FIGS.

  First, in step 2031, a document image is input as a multi-value image from a scanner which is an image input unit (3002).

  In step S2302, a binary image is generated by the binary image output unit (3004). In step S2303, the layout analysis unit (3006) converts the document image into a figure, text, table, or the like. The layout information (FIG. 15) is output after being divided into areas for various attributes.

  The layout information is displayed on the layout analysis data display unit (3016) so as to be superimposed on the input image, so that the operator can instruct an area to be encrypted with a pointing device or the like in the operator instruction unit (3017). Become. FIG. 31 is an operation screen at this time, and shows that an appropriate area is designated first, and then the encryption setting is turned “ON” for that area. The area to be encrypted (in the illustrated case, the text area) is displayed separately from the other areas, thereby informing the operator which area is to be encrypted.

  In step S2305, the document image understanding storage unit (3008) adaptively changes the storage conditions from the multi-valued image and the binary image that are the input document source images based on the layout analysis data and the operator's instruction. While creating and saving document image understanding data.

  A more specific example of document image understanding storage processing will be described with reference to the flowchart of FIG. 25 and FIGS.

  In this example, when the document image understanding storage process (step S2305) is entered, first, the ID of the area set by the operator is set in step S3101. In step S3102, the layout analysis data of each divided area is analyzed to classify the area attributes of each area.

  If the area attribute is “text” in step S3103, a binary image is selected as the entire cut-out image in step S3104, and a partial image is cut using the coordinate data of the area of the layout analysis data in step S3106.

  If the region attribute is not “text” in step S3103, the original image is selected as the entire cut-out image in step S3105, and the partial image is cut using the coordinate data of the region of the layout analysis data in step S3106.

  In step S3107, the area ID is checked to determine whether it is an encryption target area.

  If it is the encryption target area, the partial image is encrypted in step S3108 to create encrypted data, and in step S3109, the encrypted data is stored as document image understanding data.

  At this time, the encryption flag is enabled (= 1) to indicate that the data is encrypted.

  If it is not the encryption target area in step S3107, the image type of the partial image cut out in step S3110 is checked. If it is a binary image type, binary image compression processing is performed in step S3111 (for example, MMR or the like). In step S3113, the compressed partial image is stored as document image understanding data (FIG. 27).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  Also, if the result of the examination in step S3110 is a multi-value image (non-binary image such as a photographic image) type, a compression process for multi-value image (for example, JPEG) is performed and compressed in step S3112. In step S3113, the partial image is stored as document image understanding data (FIG. 27).

  In the example of the document image understanding data, as shown in FIG. 27, the appearance order in the layout analysis data of the target layout area data is set as the ID of the layout area data, and the cut width, the cut height, the image type, and the compression The method, image size, and compressed cut image are stored as partial image data.

  Next, in step S3114, it is checked whether or not there is a remaining divided area. If there is, the process returns to step S3102 and the above processing is repeated for the remaining divided areas.

  If there is no remaining divided area, the document image understanding data storage process is terminated. In this way, document image understanding data is output in step S2305.

[Eighth Embodiment]
When the area attribute is an area that can recognize characters, such as “text”, using character recognition processing to save character recognition data instead of image data, or to save both image data and character recognition data Also, the encrypted data can be stored by encrypting the character recognition data and the image data.

  A specific example will be described below as an eighth embodiment. Here, as an example, in the case of “text”, the encrypted data obtained by encrypting the character recognition result and the image data in the other area are stored as examples. FIG. 18, FIG. 19, FIG. 26, FIG. This will be described with reference to FIGS. 27, 28, and 29. FIG.

  First, in step S2301, a document image is input as a multi-valued image from a scanner which is an image input unit (2102).

  In step S2302, a binary image is generated by the binary image output unit (2104), and in step S2303, the layout analysis unit (2106) converts the document image into various attributes such as diagrams, texts, and tables. The layout information (FIG. 15) is output.

  Next, in step S2304, the document image understanding storage unit (2108) understands the document image while adaptively changing the storage conditions from the multi-value image and the binary image that are the input document original images based on the layout analysis data. Create and save data.

  At that time, encryption processing of data in a predetermined specific area is also performed.

  A more specific example of document image understanding storage processing will be described with reference to the flowchart of FIG. 26 and FIGS. 14, 15, 27, 28, and 29. FIG.

  In this example, when the document image understanding storage process (step S2305) is entered, first, in step S3201, the layout analysis data of each divided area is analyzed to classify the area attributes of each area. If the region attribute is “text” in step S3202, a binary image is selected as the recognition destination entire image in step S3203, and the partial image is selected using the coordinate data of the region of the layout analysis data in step S3204. Perform character recognition processing.

  Next, the character recognition data resulting from the character recognition in step S3205 is encrypted to create encrypted data, and in step S3206, the encrypted data is stored as document image understanding data (FIG. 29). Here, setting the encryption flag to valid (= 1) indicates that the data is encrypted.

  If the region attribute is not “text” in step S3202, the original image is selected as the entire cut-out image in step S3207, and the partial image is cut using the coordinate data of the region of the layout analysis data in step S3208.

  Then, the image type of the partial image cut out in step S3209 is checked. If the image type is a binary image type, binary image compression processing is performed in step S3210 (for example, MMR), and the compressed partial image is converted into a document in step S3211. Storage processing is performed as image understanding data (FIG. 27).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S3210 is a multi-value image type, multi-value image compression processing is performed in step S3212 (for example, JPEG), and the compressed partial image is converted into document image understanding data in step S3211. The storage process is performed as shown in FIG.

  As shown in FIG. 27, the more detailed example of the document image understanding data of the present example uses the appearance rank in the layout analysis data of the target layout area data as the layout area data ID, and the cut width and cut height. The image type, compression method, image size, and compressed cut image are stored as partial image data.

  Next, in step S3213, it is checked whether or not there is a remaining divided area. If there is, the process returns to step S3201, and the above processing is repeated for the remaining divided areas. If there is no remaining divided area, the document image understanding data storage process is terminated. In this way, document image understanding data is output in step S2305.

  In addition to the above processing, the input multi-valued image is displayed and an area to be encrypted is designated, and in the text area, both the text image and the character recognition result are encrypted. May be. A non-decipherable user (unless you enter a key that can be unlocked) displays a message saying that the area is "encrypted and cannot be displayed", and presses the key input to cancel when the area is instructed with a mouse etc. A prompt key input dialog box is displayed, and when it matches the set number key, it is displayed. Even when printing, if the area is not canceled, the area is not printed.

As described above, according to the fifth to eighth embodiments,
1) Reduction of data amount when storing document images 2) Reduction of network traffic load when distributing document images 3) Maintaining high image quality suitable for reuse when storing and distributing document images 4) Prevention of image quality degradation and information loss during output 5) Facilitation of reuse for electronic documents such as DTP (desktop publishing) 6) Improvement of security during storage and distribution of document images .

[Ninth Embodiment]
In the ninth embodiment, an example of adding a function of translating a language of a certain text in a document image into another language will be described.

  FIG. 32 shows the configuration concept and the data flow in the ninth embodiment.

  In the figure, reference numeral 4101 denotes an input document including a printed matter to be input and image data handled on a computer, 4102 denotes an image input unit for inputting the document as an image, and 4103 denotes original image data input from the image input unit. Reference numeral 4104 denotes a binary image output unit that creates a binary image from the input document image. 4105 denotes the created binary image. 4106 denotes a document based on the binary image for each attribute such as a figure, text, or table. Layout analysis unit that outputs information divided into regions, ie, layout analysis data, 4107 is layout analysis data, and 4108 is an adaptive change of storage conditions from the input document original image and binary image based on the layout analysis data The document image understanding data storage unit stores image data, character recognition data, and translation data as document image understanding data. That.

  4109 is a document image comprehension tag output unit that outputs document image comprehension data, 4110 is document image comprehension storage data, 4111 is a document image comprehension data input unit that inputs document image comprehension storage data, and 4112 is adaptive from the document image comprehension data. A document image understanding / reproducing unit for generating a reproduced document image while changing the reproduction condition is denoted by 4113, a reproduced document image, and 4114 is an image output unit for outputting the reproduced document image. 4115 is an output document, 4116 is a character recognition processing unit that recognizes characters in an arbitrary region and outputs code information as character recognition data, and 4117 is information obtained by translating an arbitrary type of language into another arbitrary type of language. This is a translation processing unit (including a dictionary for various syntax analysis for translation) that is output as translation data.

  Since the system in the above configuration is the same as that in FIG.

  Next, the flow of processing will be described with reference to the flowcharts of FIGS. 19, 4, 33, and 10, and FIGS. 32, 2, 40, 15, 16, 17, and 41.

  In the ninth embodiment, a 24-bit multi-valued image is input from a scanner (203 or 206), automatically translated into a “text” attribute area, stored, distributed over a network, and distributed to An image understanding processing system when outputting to a monochrome printer (208, 211, or 205) will be described.

  First, in step S2301, a document image is input as a multivalued image from a scanner which is an image input unit (4102).

  In step S2302, a binary image is generated by the binary image output unit (4104), and in step S2303, the layout analysis unit (4106) generates a document image for each attribute such as a figure, text, or table. The layout information (FIG. 15) is output.

  Next, in step S2304, the document image understanding storage unit (4108) understands the document image while adaptively changing the storage conditions from the multi-value image and the binary image that are the input document original images based on the layout analysis data. Create and save data. At that time, character recognition processing and translation processing of data in a predetermined specific area are also performed.

  In the present embodiment, an example in which image data in the “text” region is recognized and translated (from Japanese to English) is shown.

  A specific example of document image understanding storage processing will be described with reference to the flowchart of FIG. 33 and FIGS. 40, 15, and 16.

  In this example, when the document image understanding storage process (S2304) is entered, first, in step S4501, the layout analysis data of each divided area is analyzed to classify the area attributes of each area.

  If the region attribute is “text” in step S4502, a binary image is selected as the entire image for recognition in step S4503, and the partial image is selected using the coordinate data of the region of the layout analysis data in step S4504. Perform character recognition processing.

  In step S4505, the character recognition data is stored as document image understanding data (FIG. 16).

  Next, the character recognition data obtained as a result of character recognition in step S4506 is translated to create translation data. In step S4507, the translation data is also stored as document image understanding data (FIG. 41).

  If the region attribute is not “text” in step S4502, the original image is selected as the entire cut-out image in step S4508, and the partial image is cut using the coordinate data of the region of the layout analysis data in step S4509.

  Then, the image type of the partial image cut out in step S4510 is checked. If the image type is a binary image type, binary image compression processing is performed in step S4511 (for example, MMR), and the compressed partial image is converted into a document in step S4513. Storage processing is performed as image understanding data (FIG. 16).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S4510 is a multi-value image type, multi-value image compression processing is performed in step S4512 (for example, JPEG), and the compressed partial image is converted into document image understanding data in step S4513. The storage process is performed as shown in FIG.

  As shown in FIG. 15, the document image comprehension data example of this example is the layout area data ID, which is the appearance order in the layout analysis data of the target layout area data, and the cut width, the cut height, and the image type. The compression method, the image size, and the compressed cut image are stored as partial image data.

  Next, in step S4514, it is checked whether or not there is a remaining divided area. If there is, the process returns to step S4501, and the above processing is repeated for the remaining divided areas. If there is no remaining divided area, the document image understanding data storage process is terminated. In this way, document image understanding data is output in step S2305.

  In the present embodiment, the document image understanding data created and stored in the document image understanding storage unit (4108) is output on the network by the document image understanding data output unit (4109), and is distributed to the usage destination via the network.

  At the use destination, the same processing as in FIG. 4 described in the first embodiment is performed.

  First, in step S401, document image understanding data is input by the document image understanding data input unit (4111). In step S402, a reproduction document image is created while changing reproduction conditions adaptively from the document image understanding data.

  A specific example of document image understanding reproduction processing (S402) will be described with reference to FIGS.

  In this example, when there is translation data in a predetermined language, the translation data is automatically extracted and reproduced.

  First, in step S4601, an image serving as a background of the reproduced document image is created so as to be a white background.

  Next, the document image understanding data is analyzed in step S4602, and if the attribute of the partial area is “text” in step S4603, it is checked in step S4604 whether translation data exists in the document image understanding data. To do.

  If the translation data exists, in step S4605, the translation data is extracted from the document image understanding data, and in step S4606, the font pattern corresponding to the character code constituting the translated sentence is combined with the white background image to generate the reproduced image. create.

  If there is no translation data, original character recognition data is extracted from the document image understanding data in step S4607.

  In step S4608, the extracted original text is combined with a white background image to create a reproduction image.

  If the attribute of the partial area is not “text” in step S4603, image data is extracted from the document image understanding data in step S4609. In step S4610, a white background image is synthesized from the extracted partial image and its coordinate data. Then create a playback image.

  As an example of the reproduction synthesis process (step S4610), the process may be performed according to FIG. 10 described in the first embodiment. That is, in step S1001, the image type of the partial image is extracted from the document image understanding data. If the image type is “binary image type” in step S1002, the black and white pixels of the binary image are respectively determined in step S1003. A pseudo 24-bit conversion process is performed to convert the 24-bit multi-valued image into black and white pixels.

  In this example, when the black pixel of the binary image is 1 and the white pixel is 0, the black pixel is associated with the black pixel of the 24-bit multi-valued image, so that the black pixel has R = 0, G = 0, B The white pixels are R = 255, G = 255, and B = 255 (R is a Red component, G is a Green component, and B is a Blue component, each having a value of 8 bits).

  In step S1002, if the image type is “24-bit multi-value image type”, the partial image as it is is used for synthesis.

  Next, in step S1004, a logical operation is performed on each pixel of the partial image with respect to the base image and the partial image to create a composite image.

  When the reproduction / combination processing (steps S4606, S4608, and S4610 in FIG. 34) is completed for one partial image by the above processing, it is checked in step S4611 whether the remaining partial images are present. If so, the process returns to step S4602, and the above processing is repeated for the remaining partial areas.

  If there is no remaining partial area, the document image understanding data reproduction process is terminated.

  Next, in step S403 in FIG. 4, the reproduced image is output as an output document from the monochrome printer as the image output unit (4114).

[Tenth embodiment]
It is also possible for the operator to check the input image and layout analysis result, select a specific area from the analysis result, and instruct translation.

  A specific example will be described with reference to FIG. 19 (described in the fifth embodiment), FIG. 35, and FIG.

  First, in step 2301, a document image is input as a multi-value image from a scanner which is an image input unit (4802).

  Next, in step S2302, a binary image is generated by the binary image output unit (4804), and in step S2303, the layout analysis unit (4806) converts the document image into a figure, text, table, or the like. The layout information (FIG. 15) is output after being divided into areas for various attributes.

  The layout information is displayed so as to be superimposed on the input image by the layout analysis data display unit (4816), so that the operator can instruct an area for translation processing with a pointing device or the like in the operator instruction unit (4817). is there. FIG. 38 shows an example of an area designation screen for performing this translation processing. In the figure, the upper part of the second column is set as the translation target (area specification is turned ON).

  In step S2305, the document image understanding storage unit (4808) adaptively changes the storage conditions from the multi-value image and the binary image that are the input document source images based on the layout analysis data and the operator's instruction. While creating and saving document image understanding data.

  A specific example of document image understanding storage processing will be described with reference to the flowchart of FIG. 36 and FIGS. 40, 15, and 16.

  In this example, when the document image understanding storage process (step S2305) is entered, first, the ID of the area set by the operator is set in step S4901.

  In step S4902, the layout analysis data of each divided area is analyzed to classify the area attributes of each area. In step S4903, it is determined whether the region attribute includes text. If the region attribute includes text, a binary image is selected as a recognition image in step S4904, and the coordinate data of the region of the layout analysis data is used in step S4905. Then, character recognition processing is performed on the partial area to obtain character recognition data. In step S4906, the character recognition data is stored as document image understanding data. It should be noted that an image that does not include text, such as a photographic image, is made untranslatable from the beginning, so no problem occurs.

  In step S4907, the region ID of the partial region is checked to determine whether it is a translation target region.

  If it is not an area to be translated, the processing relating to this area is completed simply by storing the original character recognition data.

  If it is an area to be translated, translation processing is performed in step S4908, and the translation data is stored as document image understanding data in step S4909.

  In step S4903, if the region attribute is not to be translated, that is, if it does not include text, the original image is selected as the entire cut-out image in step S4910, and the coordinate data of the region of the layout analysis data is selected in step S4911. Cut out the partial image using.

  The image type of the partial image cut out in step S4912 is checked. If it is a binary image type, the binary image is compressed in step S4913 (for example, MMR), and the compressed partial image is understood in step S4915. Storage processing is performed as data (FIG. 16).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S4910 is a multi-value image type, multi-value image compression processing is performed in step S4914 (for example, JPEG), and the compressed partial image is converted into document image understanding data in step S4915. The storage process is performed as shown in FIG.

  In the example of the document image understanding data of this example, as shown in FIG. 16, the appearance order in the layout analysis data of the target layout area data is taken as the ID of the layout area data, the cut width, the cut height, the image type The compression method, the image size, and the compressed cut image are stored as partial image data.

  Next, in step S4916, it is checked whether or not there is a remaining divided area. If it exists, the process returns to step S4902, and the above processing is repeated for the remaining divided areas.

  If there is no remaining divided area, the document image understanding data storage process is terminated. In this way, document image understanding data is output in step S2305.

[Eleventh embodiment]
By providing a plurality of language translation means, it is possible to have a plurality of translation data for one original text recognition data.

[Twelfth embodiment]
It is also possible to translate from a plurality of translation destinations into the language selected by the operator.

  A specific example will be described with reference to FIG. 19 again, and further with reference to FIGS.

  First, in step S2301, a document image is input as a multi-value image from a scanner which is an image input unit (4802).

  Next, the translation language is instructed by the operator instruction unit (4817).

  For example, it is assumed that the operator designates “Japanese” as the translation target language for a document whose original text is English. In this example, there is one translation destination, but a plurality of languages can be designated.

  In step S2302, a binary image is generated by the binary image output unit (4804). Based on the binary image output unit (4804), the layout analysis unit (4806) converts the document image into various kinds of figures, texts, tables, and the like. The layout information is divided into areas for each attribute and layout information (FIG. 15) is output.

  In step S2305, the document image understanding storage unit (4808) adaptively changes the storage conditions from the multi-value image and the binary image that are the input document source images based on the layout analysis data and the operator's instruction. While creating and saving document image understanding data.

  The instruction from the operator here is a process of displaying the layout-analyzed contents as shown in FIG. 39 and selecting a translation destination language. In this embodiment, the object to be translated is a text area, but an object including characters such as a table may be the object.

  A specific example of document image understanding storage processing will be described with reference to the flowchart of FIG. 37, FIG. 40, FIG. 15, FIG. 16, FIG.

  In this example, when the document image understanding storage process (step S2305) is entered, first, the translation destination language set by the operator in step S5001 is set, and preparation for the translation process is performed.

  In step S5002, the layout analysis data of each divided area is analyzed to classify the area attributes of each area.

  In step S5003, it is determined whether the region attribute is “text”. If so, the process advances to step S5004 to select a binary image as a recognition image for the area, and in step S5005, character recognition processing is performed on the partial area using the coordinate data of the area of the layout analysis data. Get the data. In step S5006, the character recognition data is stored as document image understanding data. Next, the process proceeds to step 5008 to perform translation processing, and the translation result is also saved in step 5009 (FIG. 41).

  On the other hand, if it is determined in step S5003 that the region attribute is not “text”, the original image is selected as the entire cut-out image in step S5010, and the partial image is selected using the coordinate data of the region of the layout analysis data in step S5011. cut out.

  Next, the image type of the image portion cut out in step S5012 is checked. If the image type is a binary image type, a binary image compression process is performed in step S5013 (for example, MMR), and the compressed partial image is converted into a document in step S5015. Storage processing is performed as image understanding data (FIG. 16).

  At this time, by recording the compression method in the document image understanding data, the compressed image can be decoded in the document image understanding reproduction process.

  If the result checked in step S5012 is a multi-valued image type, multi-valued image compression processing (for example, JPEG) is performed in step S5014, and the compressed partial image is converted into document image understanding data (in step S1015). The storage process is performed as shown in FIG.

  In a more detailed example of document image understanding data (non-text image) of this example, as shown in FIG. 16, the appearance order in the layout analysis data of the target layout area data is set as the layout area data ID, The cut width, the cut height, the image type, the compression method, the image size, and the compressed cut image are stored as partial image data.

  Next, in step S5016, it is checked whether or not there is a remaining divided area. If there is, the process returns to step S5002, and the above processing is repeated for the remaining divided areas. If there is no remaining divided area, the document image understanding data storage process is terminated. In this way, document image understanding data is output in step S2305.

As described above, according to the ninth to twelfth embodiments,
1) Reduction of data amount when storing document images 2) Reduction of network traffic load when distributing document images 3) Maintaining high image quality suitable for reuse when storing and distributing document images 4) Prevention of image quality degradation and information loss at the time of output 5) Ease of reuse for electronic documents such as DTP (desktop publishing) 6) Improvement of convenience at the time of global document distribution, etc. .

  Note that the present invention can be applied to a system including a plurality of devices (for example, a host computer, an interface device, a reader, and a printer), and a device (for example, a copying machine and a facsimile device) including a single device. You may apply to.

  FIG. 2 shows an example in which information is exchanged via a network. However, the Internet may be adopted as this network, and the present invention is not limited by the form of the network itself. Absent.

  The above embodiments can be broadly divided into the first to fourth embodiments, the fifth to eighth embodiments, and the ninth to twelfth embodiments. Of course, it may be.

  In addition, an object of the present invention is to supply a storage medium (or recording medium) that records a program code of software that realizes the functions of the above-described embodiments to a system or apparatus, and to perform a computer (or CPU) of the system or apparatus. Needless to say, this can also be achieved by reading and executing the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present invention. Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an operating system (OS) running on the computer based on the instruction of the program code. It goes without saying that a case where the function of the above-described embodiment is realized by performing part or all of the actual processing and the processing is included.

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

  As described above, according to the first invention, it is possible to reduce the amount of data while maintaining the document layout, and to suppress the deterioration of the image quality when reproducing and outputting.

  Further, according to the second invention, in addition to the first invention, the security can be improved.

  And according to 3rd invention, in addition to the said 1st invention, it also becomes possible to absorb the difference of a natural language about the text in an image. Therefore, it becomes possible to share information regardless of whether inside or outside the country.

It is a figure which shows the flow of the data in 1st Embodiment. It is a figure which shows the system configuration | structure of 1st Embodiment. It is a flowchart which shows the procedure from the image input in 1st Embodiment to the output process of document image understanding preservation | save data. It is a flowchart which shows the process sequence from document image understanding preservation | save data input in each embodiment to reproduction | regeneration image output. It is a flowchart which shows the document image understanding preservation | save process of 1st Embodiment. It is a flowchart which shows the text discrimination | determination process in 1st Embodiment. It is a flowchart of the document image understanding preservation | save process of 1st Embodiment. It is a flowchart of the document image understanding preservation | save process of 1st Embodiment. It is a flowchart of the document image understanding reproduction process of the first embodiment. It is a flowchart of the reproduction | regeneration image composition process of 1st Embodiment. It is a flowchart of the document image understanding preservation | save process in 2nd Embodiment. It is a flowchart of the document image understanding preservation | save process in 3rd Embodiment. It is a flowchart of the document image understanding reproduction process of 3rd, 4th embodiment. It is a figure which shows the structure of the document image understanding data of 1st Embodiment. It is a figure which shows the structure of the layout analysis data of 1st Embodiment. It is a figure which shows the structure of the image data of 1st Embodiment. It is a figure which shows the structure of the character recognition data of 1st Embodiment. It is a figure which shows the flow of the data in 5th Embodiment. It is a flowchart from the image input of 5th Embodiment to document image understanding data output. It is a flowchart of the document image understanding preservation | save process of 5th Embodiment. It is a flowchart of the document image understanding reproduction process of 5th Embodiment. It is a figure which shows the flow of the data in 6th Embodiment. It is a flowchart of the document image understanding preservation | save process of 6th Embodiment. It is a figure which shows the flow of the data in 7th Embodiment. It is a flowchart of the document image understanding preservation | save process of 7th Embodiment. It is a flowchart of the document image understanding preservation | save process of 8th Embodiment. It is a figure which shows the structure of the image data of 5th Embodiment. It is a figure which shows the structure of the character recognition data of 5th Embodiment. It is a figure which shows the structure of the encryption character recognition data of 5th Embodiment. It is a figure which shows the example of GUI of 6th Embodiment. It is a figure which shows the example of GUI of 7th Embodiment. It is a figure which shows the flow of the data in 9th Embodiment. It is a flowchart of the document image understanding preservation | save process of 9th Embodiment. It is a flowchart of the document image understanding reproduction process of 9th Embodiment. It is a figure which shows the flow of the data in 10th Embodiment. It is a flowchart of the document image understanding preservation | save process of 11th Embodiment. It is a flowchart of the document image understanding preservation | save process of 11th Embodiment. It is a figure which shows the example of GUI of 10th Embodiment. It is a figure which shows the example of GUI of 11th Embodiment. It is a figure which shows the structure of the document image comprehension data of 9th Embodiment. It is a figure which shows the structure of the translation data of 9th Embodiment.

Claims (7)

Input means for inputting multivalued image data of a document;
Binary image generation means for generating binary image data from the input multi-value image data;
A layout analysis unit that divides the generated binary image data into regions for each attribute, and generates layout information for each of the divided regions;
First partial image cutting means for cutting a partial image of a corresponding area from the binary image data for a text attribute area based on the layout information;
Based on the layout information, second partial image cutting means for cutting a partial image of a corresponding area from the multi-valued image data for a non-text attribute area;
Encryption target attribute setting means for setting an attribute to be encrypted based on an instruction from the operator;
Of the partial images cut by the first partial image cutting unit or the second partial image cutting unit, the partial image corresponding to the attribute area set as the encryption target by the encryption target attribute setting unit is encrypted. An encryption means for generating encrypted data;
The first partial image cutout corresponding to the encrypted data generated by the encryption means corresponding to the encryption target area and the attribute area not set as the encryption target by the encryption target attribute setting means Means for storing the partial image cut by the second partial image cutting means, and the layout information;
An image processing apparatus comprising: Input means for inputting multivalued image data of a document;
Binary image generation means for generating binary image data from the input multi-value image data;
A layout analysis unit that divides the generated binary image data into regions for each attribute, and generates layout information for each of the divided regions;
First partial image cutting means for cutting a partial image of a corresponding area from the binary image data for a text attribute area based on the layout information;
Character recognition means for recognizing characters for a partial image corresponding to the text attribute region cut by the first partial image cutting means;
Based on the layout information, second partial image cutting means for cutting a partial image of a corresponding area from the multi-valued image data for a non-text attribute area;
When the encryption target area is the text attribute area, both the character recognition data of the encryption target area recognized by the character recognition means and the partial image cut by the first partial image cutting means are used. Encryption means for encrypting and generating encrypted data;
Storage means for storing the encrypted data generated by the encryption means, the partial image cut by the second partial image cutting means, and the layout information;
An image processing apparatus comprising: The image processing apparatus of the described the encrypted data and the partial image and the layout information stored by the storing means, an output means for outputting to another device, to claim 1 or 2, further comprising. A binary image generation means for generating binary image data from multi-value image data of a document;
A layout analysis unit that divides the generated binary image data into regions for each attribute and generates layout information for each of the divided regions; and
A first partial image cutting step for cutting a partial image of a corresponding region from the binary image data for a text attribute region based on the layout information;
A second partial image cutting step for cutting a partial image of a corresponding area from the multi-valued image data for a non-text attribute area based on the layout information;
An encryption target attribute setting unit sets an attribute to be encrypted based on an instruction from an operator, and an encryption target attribute setting step;
Of the partial images cut by the first partial image cutting step or the second partial image cutting step, the portion corresponding to the attribute region set as the encryption target in the encryption target attribute setting step An encryption process for encrypting the image and generating encrypted data;
The storage means includes the encrypted data generated in the encryption step corresponding to the encryption target region and the attribute region not set as the encryption target in the encryption target attribute setting step . A storing step of storing the partial image cut out in the one partial image cutting step or the second partial image cutting step and the layout information;
An image processing method comprising: A binary image generation means for generating binary image data from multi-value image data of a document;
A layout analysis unit that divides the generated binary image data into regions for each attribute and generates layout information for each of the divided regions; and
A first partial image cutting step for cutting a partial image of a corresponding region from the binary image data for a text attribute region based on the layout information;
A character recognizing step for recognizing characters for a partial image corresponding to the text attribute region cut out in the first partial image cutting step;
A second partial image cutting step for cutting a partial image of a corresponding area from the multi-valued image data for a non-text attribute area based on the layout information;
When the encryption unit is the text attribute region, the encryption unit recognizes the character recognition data of the encryption target region recognized in the character recognition step and the portion cut out in the first partial image cutting step An encryption process for encrypting both the image and generating encrypted data;
A storage unit that stores the encrypted data generated in the encryption step, the partial image cut out in the second partial image cutting step, and the layout information;
An image processing method comprising: A storage medium for storing a computer program that can be read and executed by a computer,
Computer
Binary image generation means for generating binary image data from multivalued image data of a document;
Layout analysis means for dividing the generated binary image data into areas for each attribute and generating layout information for each of the divided areas;
First partial image cutting means for cutting a partial image of a corresponding area from the binary image data for a text attribute area based on the layout information;
Second partial image cutting means for cutting a partial image of a corresponding region from the multi-valued image data for a non-text attribute region based on the layout information;
Encryption target attribute setting means for setting an attribute to be encrypted based on an instruction from the operator,
Of the partial images cut by the first partial image cutting unit or the second partial image cutting unit, the partial image corresponding to the attribute area set as the encryption target by the encryption target attribute setting unit is encrypted. Encryption means for generating encrypted data;
The first partial image cutout corresponding to the encrypted data generated by the encryption means corresponding to the encryption target area and the attribute area not set as the encryption target by the encryption target attribute setting means Storing means for storing the partial image clipped by the second partial image cutting means and the layout information;
A storage medium for storing a computer program for functioning as a computer. A storage medium storing a computer program that can be read and executed by a computer,
Computer
Binary image generation means for generating binary image data from multivalued image data of a document;
Layout analysis means for dividing the generated binary image data into areas for each attribute and generating layout information for each of the divided areas;
First partial image cutting means for cutting a partial image of a corresponding area from the binary image data for a text attribute area based on the layout information;
Character recognition means for recognizing characters for a partial image corresponding to the region of the text attribute cut by the first partial image cutting means;
A second partial image cutting means for cutting a partial image of a corresponding area from the multi-valued image data for a non-text attribute area based on the layout information;
When the encryption target area is the text attribute area, both the character recognition data of the encryption target area recognized by the character recognition means and the partial image cut by the first partial image cutting means are used. Encryption means for encrypting and generating encrypted data;
Storage means for storing the encrypted data generated by the encryption means, the partial image cut by the second partial image cutting means, and the layout information;
A storage medium storing a computer program for functioning as a computer.

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