Patent Publication Number: US-7596271-B2

Title: Image processing system and image processing method

Description:
FIELD OF THE INVENTION 
   The present invention relates to an image processing system and image processing method, which convert input image information into a predetermined format. 
   BACKGROUND OF THE INVENTION 
   Along with a recent growing interest in environmental issues, move to paperless offices has rapidly been promoted. For this purpose, there is conventionally known a document management system which reads paper documents accumulated in binders by using a scanner, converts the read images into portable document format (to be abbreviated as “PDF” hereinafter) data, and accumulates them in an image storage device as a database. 
   Under these circumstances, even a forbidden document can sometimes easily be copied and carried out. To know when information that should not be printed has been printed and who has printed it, a method of saving an input image as image data is known (e.g., Japanese Patent Laid-Open No. 6-270477). 
   Another method is also known in which input image data is compared with a predetermined image, thereby determining whether the data is information which should not be printed (e.g., Japanese Patent Laid-Open No. 6-178066). 
   However, the former method requires an image storage area with an enormous capacity to accumulate image data each having a large data amount in the image processing system. The latter method can cope with a change in various directions. However, to inhibit printing of data matching a given keyword, patterns corresponding to various volumes and sizes must be generated in correspondence with the same keyword, resulting in a cumbersome determination process. 
   SUMMARY OF THE INVENTION 
   The present invention has been proposed to solve the conventional problems, and has as its object to provide an image processing system and image processing method which can specify and save an input image which matches a predetermined pattern and also simplify the determination process. 
   In order to solve the above-described problems, according to the present invention, there is provided an image processing system comprising: 
   an input unit adapted to input image information: 
   a vectorization unit adapted to generate vector data from the image information input by the input unit; 
   an object information input unit adapted to input predetermined object information; 
   a determination unit adapted to determine whether information similar to the object information input by the object information input unit is contained in the vector data generated by the vectorization unit; and 
   a storage unit adapted to, when the determination unit determines that the object information is contained in the vector data, store the vector data in correspondence with specific property information. 
   In order to solve the above-described problems, according to the present invention, there is provided an image processing method comprising: 
   an input step of inputting image information: 
   a vectorization step of generating vector data from the image information input in the input step; 
   an object information input step of inputting predetermined object information; 
   a determination step of determining whether information similar to the object information input in the object information input step is contained in the vector data generated in the vectorization step; and 
   a storage step of, when it is determined in the determination step that the object information is contained in the vector data, storing the vector data in correspondence with specific property information. 
   In order to solve the above-described problems, according to the present invention, there is provided an image processing system comprising: 
   an input unit adapted to input image information; 
   a holding unit adapted to hold a predetermined keyword; 
   an identification unit adapted to identify keyword information contained in the image information input by the input unit; 
   a determination unit adapted to determine whether the keyword held by the holding unit matches the keyword information identified by the identification unit; and 
   a storage unit adapted to, when the determination unit determines that the keyword matches the keyword information, store the image information input by the input unit in correspondence with specific property information. 
   In order to solve the above-described problems, according to the present invention, there is provided an image processing method comprising: 
   an input step of inputting image information; 
   a holding step of holding a predetermined keyword; 
   an identification step of identifying keyword information contained in the image information input in the input step; 
   a determination step of determining whether the keyword held in the holding step matches the keyword information identified in the identification step; and 
   a storage step of, when it is determined in the determination step that the keyword matches the keyword information, storing the image information input in the input step in correspondence with specific property information. 
   Other feature and advantages of the present invention will be apparent from the following description taken in conjunction with the accompanying drawings, in which like references characters designate the same or similar parts throughout the figures thereof. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The accompanying drawings, which are incorporates in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principle of the invention. 
       FIG. 1  is a block diagram showing the arrangement of an image processing system according to an embodiment of the present invention; 
       FIG. 2  is a block diagram showing the arrangement of an MFP  100  according to the embodiment of the present invention; 
       FIG. 3  is a flowchart for explaining the image process procedures of the image processing system according to the embodiment of the present invention; 
       FIG. 4  is a view showing a state wherein read image data of one page is segmented into a plurality of blocks by a block selection process by determining properties; 
       FIG. 5  is a table showing an example of block information of respective blocks obtained by the block selection process; 
       FIG. 6  is a view for explaining a point with a maximal curvature; 
       FIG. 7  is a view for explaining an example wherein an outer outline which is close to an inner outline or another outer outline is expressed as a line with a given width; 
       FIG. 8  is a flowchart for explaining process procedures executed until vector data are grouped for each graphic object; 
       FIG. 9  is a flowchart for explaining process procedures of detecting a graphic element; 
       FIG. 10  is a view showing the data structure of an intermediate data format as a result obtained by converting image data of one page by the block selection process (step S 302 ) and vectorization process (step S 304 ); 
       FIG. 11  is a flowchart for explaining schematic procedures of the overall conversion process into application data; 
       FIG. 12  is a flowchart for explaining detailed process procedures of a document structure tree generation process (step S 802 ); 
       FIGS. 13A and 13B  are views for explaining the outline of a document structure tree; 
       FIG. 14  is a block diagram showing the detailed arrangement of a data processing device  115  in the MFP  100  according to the embodiment; 
       FIG. 15  is a flowchart for explaining a vector image accumulation process in the MFP  100  according to the embodiment; 
       FIG. 16  is a view for explaining a detailed example of a keyword selection process in step S 1501 ; 
       FIG. 17  is a flowchart for explaining operation procedures of a management PC  101  to save vector data; 
       FIG. 18  is a flowchart for explaining keyword generation procedures of the image processing system according to the embodiment; and 
       FIG. 19  is a view showing the difference vector between a vector image as Key and scanned Image. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
   An image processing system and image processing method according to a preferred embodiment of the present invention will be described below with reference to the accompanying drawings. 
     FIG. 1  is a block diagram showing the arrangement of an image processing system according to an embodiment of the present invention. The image processing system shown in  FIG. 1  is implemented in an environment in which offices  10  and  20  are connected via an network  104  such as the Internet. 
   A digital multifunction peripheral (MFP)  100 , a management PC  101  which controls the MFP  100 , a client PC  102 , a document management server  106   a , a database  105   a , and a proxy server  103   a  are connected to a LAN  107  formed in the office  10 . The MFP  100  can be implemented by, e.g., a copying machine or facsimile apparatus having a multifunction. A document management server  106   b , database  105   b , and proxy server  103   b  are connected to a LAN  108  formed in the office  20 . The client PC  102  comprises an external storage unit, search image input unit, and search result output unit. The LAN  107  and the LAN  108  in the office  20  are connected to the network  104  such as the Internet via the proxy servers  103   a  and  103   b , respectively. 
   The MFP  100  in this embodiment is in charge of an image reading process of optically reading a paper document and converting it into an image signal and some of image processes for the read image signal, and inputs the image signal to the management PC  101  via a LAN  109 . The management PC  101  can also be implemented by a normal PC and incorporates an image storage unit, image processing unit, display unit, and input unit. The management PC  101  may partially or wholly be integrated with the MFP  100 . 
     FIG. 2  is a block diagram showing the arrangement of an MFP  100  according to the embodiment of the present invention. An operator&#39;s instruction to the MFP  100  is input through an input device  113  such as keys equipped on the MFP  100  or an input device including a keyboard and mouse of a management PC  101 . The series of operations is controlled by a controller in a data processing device  115 . 
   Referring to  FIG. 2 , an authentication device  118  requests the user to input authentication information (e.g., a personal ID or password) and issues for the operator an access permission to the MFP  100  on the basis of the authentication information such as the user&#39;s personal ID or password input from the input device  113 . If it is determined on the basis of the authentication result by the authentication device  118  that the user is permitted to access, an image reading device  110  including an auto document feeder (to be abbreviated as an “ADF” hereinafter) irradiates a document image on each of one or a plurality of stacked documents with light from an internal light source, forms an image of light reflected by the document on a solid-state image sensing element via a lens, and obtains an image reading signal in the raster order as image information at a resolution of, e.g., 600 dpi from the solid-state image sensing element. When a normal copying function is used, the data processing device  115  executes an image process of that image signal to convert it into a recording signal. In case of a multi-copying process, recording data of one page is temporarily stored in a storage device  111  and sequentially output to a printing device  112 , and then images are formed on paper sheets. 
   Print data output from a client PC  102  is input from a LAN  107  to the MFP  100  and to the data processing device  115  via a network I/F  114  and converted into recordable raster data by the data processing device  115 . The raster data is then input to the printing device  112  to form a recording image on a paper sheet. 
   Status of operation inputs and image data whose process is underway are displayed on a display device  116  of the MFP  100  or on the monitor of the management PC  101  or client PC  102 . The storage device  111  stores image data which is read by the image reading device  110  and processed by the data processing device  115 . The storage device  111  can also be controlled from the management PC  101 . Data exchange and control between the MFP  100  and management PC  101  are done by using the network I/F  114  and a directly connected LAN  109 . 
   Scan data can also be transmitted to a set transmission destination by using a facsimile (FAX) I/F  117 . Received data from the FAX I/F  117  can also be printed by using the printing device  112 . The FAX reception data can also be transferred to a set destination through the network I/F  114 . Data received from the network I/F  114  can also be transferred by using the FAX I/F  117 . 
   [Outline of Reading Process] 
   An outline of the entire image process of the image processing system according to the embodiment of the present invention will be described below.  FIG. 3  is a flowchart for explaining the image process procedures of the image processing system according to the embodiment of the present invention. A process of acquiring image information by reading a paper document will be described with reference to the flowchart in  FIG. 3 . 
   The image reading device  110  of the MFP  100  is operated to scan one document in a raster order to obtain an, e.g., 8-bit image signal of 600 dpi (image information input process: step S 301 ). This image signal undergoes a pre-process by the data processing device  115  and is saved as image data of one page in the storage device  111 . 
   A CPU in the data processing device  115  or the CPU of the management PC  101  separates text/line art portions and halftone image portions from the image signal stored in the storage device  111 . Each text portion is further separated into blocks combined as clusters for the respective paragraphs or tables and graphics formed of lines. The separated parts are segmented. On the other hand, each image portion expressed by halftone is segmented into independent objects for the respective so-called blocks such as an image portion and background portion which are separated into rectangles (BS process: step S 302 ). 
   An OCR process is executed to recognize the character size, style, and font of the text block (step S 303 ). The data is converted into font data (including character codes) which are visually faithful to characters read by scanning the document so that vector data is generated (step S 304 ). For a table or graphic block formed of lines, the outline is specified to generate vector data. An image block is processed as an individual JPEG file as image information. These vectorization processes are done for the respective objects, and layout information of each object is saved. With the process in step S 304 , image information is converted into vector data so that the data is converted into a digital file close to the original digital file. 
   Data whose vector data is permitted to reuse is converted into application data having a format such as an rtf file processible by general-purpose document creation software (step S 305 ) and stored in the storage device  111  as a digital file (step S 306 ). 
   Each process block will be described below in detail. 
   The block selection (BS) process in step S 302  will be described below. 
   [Block Selection Process] 
     FIG. 4  is a view showing a state wherein read image data of one page is segmented into a plurality of blocks by a block selection process by determining properties. More specifically, in the block selection process, image data  41  of one page read in step S 301  is recognized as a cluster  42  of objects, and the properties of the respective blocks are determined as text (TEXT), photo (PHOTO), line (LINE), table (TABLE), and the like so that the image data is segmented into regions (blocks) having different properties. 
   An embodiment of the block selection process will be described below. 
   An input image is binarized to monochrome image data. Outline tracking is executed to extract a cluster of pixels surrounded by black pixels. For a black pixel cluster having a large area, outline tracking is executed for white pixels in that cluster to extract a cluster of white pixels. A cluster of black pixels is also extracted recursively from a white pixel cluster having a predetermined area or more. The above-described process is executed for a document having black characters printed on a white background. A document of another type can be processed in the same way by setting the color corresponding to the background to “white” and that corresponding to an object to “black”. 
   The obtained black pixel clusters are classified into regions having different properties in accordance with their sizes and shapes. For example, a pixel cluster having an aspect ratio of almost 1 and a size in a predetermined range is determined as a pixel cluster corresponding to a character. Furthermore, a portion where neighboring characters regularly line up and can be regarded as a group is determined as a text region. A low-profile pixel cluster is categorized as a line region. A range occupied by a black pixel cluster which includes rectangular white pixel clusters which regularly line up and have a predetermined size or more is categorized as a table region. A region where pixel clusters with indeterminate forms are distributed is categorized as a photo region. A cluster with an arbitrary shape is categorized as a graphic region. With this process, more advanced limits can be imposed for reuse of digital data created by reading one document. 
     FIG. 5  is a table showing an example of block information of respective blocks obtained by the block selection process. Information of each block shown in  FIG. 5  is used as information for vectorization or search to be described later. 
   [Vectorization Process] 
   The vectorization process in step S 304  in  FIG. 3  will be described next. For a text block, a character recognition process is executed for each character. 
   &lt;&lt;Character Recognition&gt;&gt; 
   For the character recognition process, in this embodiment, an image extracted for each character is recognized by using one of pattern matching methods, thereby obtaining a corresponding character code. In this recognition process, an observation feature vector obtained by converting a feature obtained from a character image into a several-ten-dimensional numerical value string is compared with a dictionary feature vector obtained in advance for each character type, and a character type with a shortest distance is output as a recognition result. Various known methods are available for feature vector extraction. For example, a method of dividing a character into a mesh pattern and counting character lines in respective meshes as line elements depending on their directions to obtain a (mesh count)-dimensional vector as a feature can be used. 
   When character recognition is to be executed for a text region extracted by the block selection process (step S 302 ), the writing direction (horizontal or vertical direction) of the region is determined. Lines are extracted in the direction. Then, character images are obtained by extracting characters. In determining the writing direction (horizontal or vertical direction), horizontal and vertical projections of pixel values in that region are calculated. If the variance of the horizontal projection is larger than that of the vertical projection, the region is determined as a horizontal writing region. Otherwise, the region is determined as a vertical writing region. 
   Decomposition into character strings and characters is done in the following way. For horizontal writing, lines are extracted by using the horizontal projection. In addition, characters are extracted on the basis of the vertical projection for each extracted line. For a vertical writing text region, the relationship between “horizontal” and “vertical” is reversed. The character size can be detected on the basis of the extracted size. 
   &lt;&lt;Font Recognition&gt;&gt; 
   A plurality of sets of dictionary feature vectors for the number of character types used in character recognition are prepared in correspondence with character shape types, i.e., font types, and a font type is output together with a character code upon matching, thus recognizing the font of a character. 
   &lt;&lt;Vectorization of Character&gt;&gt; 
   In this embodiment, in vectorizing a character, using a character code and font information obtained by the above-described character recognition and font recognition, the information of a character portion is converted into vector data by using outline data prepared in advance. When an input document image is a color image, the color of each character is extracted from the color image and recorded together with vector data. 
   With the above processes, image information which belongs to a text block can be converted into vector data with a nearly faithful shape, size, and color. Hence, high-quality character data can be handled. 
   &lt;&lt;Vectorization of Non-Text Portion&gt;&gt; 
   For a region which is determined as a drawing, line, or table region by the block selection process in step S 302 , the outline of each extracted pixel cluster is converted into vector data. More specifically, a point sequence of pixels which form an outline is divided into sections at a point considered as a corner, and each section is approximated by a partial line or curve. “Corner” indicates a point where the curvature is maximal. 
     FIG. 6  is a view for explaining a point with a maximal curvature. As shown in  FIG. 6 , a chord is drawn between points Pi−k and Pi+k separated k points from an arbitrary point Pi to the left and right. A point with a maximal curvature is obtained as a point where the distance between the chord and the point Pi becomes maximal. Let R be the chord length/arc length between Pi−k and Pi+k. Then, a point where the value R is equal to or smaller than a threshold value can be regarded as a corner. Sections obtained by dividing the line at corners can be vectorized by using a method of least squares with respect to a point sequence for a line and a ternary spline function for a curve. 
   When the subject has an inner outline, it is similarly approximated by a partial line or curve by using a point sequence of a white pixel outline extracted by the block selection process. 
   As described above, when partial line approximation of outlines is used, the outline of a graphic with an arbitrary shape can be vectorized. When the input document is a color document, the color of each graphic is extracted from the color image and is recorded together with vector data. 
     FIG. 7  is a view for explaining an example wherein an outer outline which is close to an inner outline or another outer outline is expressed as a line with a given width. When an outer outline is close to an inner outline or another outer outline in a given section, as shown in  FIG. 7 , the two outlines can combined and expressed as a line with a given width. More specifically, lines are drawn from points Pi on a given outline to points Qi on another outline such that two corresponding points have the shortest distance. When distances PQi maintain a predetermined value or less on the average, the section of interest is approximated by a line or curve using PQi middle points as a point sequence, and the average value of the distances PQi is set as the width of the line or curve. A line or a table ruled line as a set of lines can efficiently be expressed by vector data as a set of lines having a given width, as described above. 
   In vectorization using the character recognition process for a text block, a character which has the shortest distance from a dictionary as a result of the character recognition process is used as a recognition result, as described above. When this distance is equal to or larger than a predetermined value, the recognition result does not always match an original character, and a wrong character having a similar shape is often recognized. In this embodiment, therefore, such character is handled in the same manner as a general line art, as described above, and converted into outline data. That is, even a character that causes a recognition error in the conventional character recognition process can be vectorized on the basis of outline data which is visually faithful to image data without being vectorized to a wrong character. In this embodiment, a block which is determined as a photo is not vectorized and is output as image data without any process. 
   [Graphic Recognition] 
   A process of grouping vectorized partial lines for each graphic object after the outline of a graphic with an arbitrary shape is vectorized, as described above, will be described below. 
     FIG. 8  is a flowchart for explaining process procedures executed until vector data are grouped for each graphic object. Initial and terminal points of each vector data are calculated (step S 801 ). Using the initial and terminal point information of respective vectors, a graphic element is detected (step S 802 ). Detecting a graphic element is to detect a closed graphic formed by partial lines. Detection is executed by applying the principle that each vector which forms a closed shape has vectors coupled to its two ends. 
   Next, other graphic elements or partial lines present in the graphic element are grouped to set one graphic object (step S 803 ). If any other graphic elements or partial lines are not present in the graphic element, the graphic element is set as a graphic object. 
     FIG. 9  is a flowchart for explaining process procedures of detecting a graphic element. Unwanted vectors each having two ends unconnected to other vectors are removed from vector data to extracted closed graphic forming vectors (step S 901 ). The initial point of a vector of interest of the closed graphic forming vectors is set as a start point, and vectors are sequentially tracked clockwise. This tracking is executed until returning to the start point. All passing vectors are grouped as a closed graphic which forms one graphic element (step S 902 ). All closed graphic forming vectors present in the closed graphic are also grouped. The initial point of a vector which is not grouped yet is set as a start point, and the above process is repeated. Finally, of the unwanted vectors removed in step S 901 , those which join the vectors grouped as the closed graphic in step S 902  are detected and grouped as one graphic element (step S 903 ). 
   With the above process, a graphic block can be handled as an independently reusable graphic object. 
   [Conversion Process into Application Data] 
     FIG. 10  is a view showing the data structure of a file having an intermediate data format as a result obtained by converting image data of one page by the block selection process (step S 302 ) and the vectorization process (step S 304 ). The data format shown in  FIG. 10  is called a document analysis output format (DAOF). That is,  FIG. 10  shows the DAOF data structure. 
   Referring to  FIG. 10 , reference numeral  1001  denotes a Header which holds information about document image data to be processed. Reference numeral  1002  denotes a layout description data field which holds property information and rectangular block address information of blocks in the document image data, which are recognized for the properties such as TEXT (text), TITLE (title), CAPTION (caption), LINEART (line art), PICTURE (natural image), FRAME (frame), and TABLE (table). 
   Such DAOF data itself is sometimes saved as a file in place of intermediate data. However, in the state of a file, individual objects cannot be reused by a general document creation application. A process of converting DAOF data into application data (step S 306 ) will be described next in detail. 
     FIG. 11  is a flowchart for explaining schematic procedures of the overall conversion process into application data. DAOF data is input (step S 1101 ). A document structure tree serving as a base of application data is generated (step S 1102 ). Actual data in the DAOF are input on the basis of the generated document structure tree to generate actual application data (step S 1103 ). 
     FIG. 12  is a flowchart for explaining detailed process procedures of the document structure tree generation process (step S 1102 ).  FIGS. 13A and 13B  are views for explaining the outline of the document structure tree. As the basic rule of overall control, the flow of processes transits from a microblock (single block) to a macroblock (a set of blocks). In the following description, a block indicates both a microblock and macroblock. 
   Regrouping is done for blocks on the basis of association in the vertical direction (step S 1102   a ). Immediately after the start, determination is done for each microblock. Association can be defined when the distance between blocks is small, and blocks widths (heights in case of the horizontal direction) almost equal. The pieces of information of distances, widths, and heights are extracted with reference to the DAOF. 
     FIG. 13A  shows an actual page configuration, and  FIG. 13B  shows the document structure tree of the page. As a result of grouping in step S 1102   a , T 3 , T 4 , and T 5  form one group V 1 , and T 6  and T 7  form one group V 2 . These groups are generated as groups which belong to the same layer. 
   The presence/absence of a vertical separator is checked (step S 1102   b ). Physically, a separator is an object which has a line property in the DAOF. Logically, a separator is an element which explicitly divides blocks in an application. When a separator is detected, the groups are re-divided in the same layer. 
   It is determined by using the group length whether no more divisions can be present (step S 1102   c ). For example, it is determined whether the grouping length in the vertical direction equals the page height. If the group length in the vertical direction equals the page height (YES in step S 1102   c ), document structure tree generation is ended. In, e.g., the structure shown in  FIGS. 13A and 13B , no separator is present, and the group height does not equal the page height. Since No in step S 1102   c , the flow advances to step S 1102   d.    
   In step S 1102   d , regrouping is done for blocks on the basis of association in the horizontal direction. Even in this regrouping, the first determination immediately after the start is done for each microblock. Definitions of association and its determination information are the same as those in the vertical direction. In, e.g., the structure shown in  FIGS. 13A and 13B , T 1  and T 2  generate a group H 1 , and V 1  and V 2  generate a group H 2 . The group H 1  is generated as a group one level higher than T 1  and T 2 . The group H 2  is generated as a group one level higher than V 1  and V 2 . The groups H 1  and H 2  belong to the same layer. 
   The presence/absence of a horizontal separator is checked (step S 1102   e ). Since a separator S 1  is present in  FIGS. 13A and 13B , it is registered in the tree so that the layers H 1 , S 1 , and H 2  are generated. It is determined by using the group length whether no more divisions are present (step S 1102   f ). For example, it is determined whether the grouping length in the horizontal direction equals the page width. If the group length in the horizontal direction equals the page width (YES in step S 1102   f ), document structure tree generation is ended. If the group length in the horizontal direction does not equal the page width (NO in step S 1102   f ), the flow returns to step S 1102   a  to repeat the process from association check in the vertical direction in the layer higher one level. In, e.g., the structure shown in  FIGS. 13A and 13B , since the division width equals the page width, the process is ended. Finally, an uppermost layer V 0  representing the entire page is added to the document structure tree. 
   After the document structure tree is completed, application data is generated in step S 1103  on the basis of the information of the document structure tree. A practical example for the structure shown in  FIGS. 13A and 13B  will be explained below. 
   Since H 1  includes the two blocks T 1  and T 2  in the horizontal direction, it is output as two columns. Internal information of T 1  (text or image as the character recognition result with reference to the DAOF) is output. Then, a new column is set, and internal information of T 2  is output. After that, S 1  is output. Since H 2  includes the two blocks V 1  and V 2  in the horizontal direction, it is output as two columns. Internal information of V 1  is output in the order of T 3 , T 4 , and T 5 . Then, a new column is set, and internal information of V 2  is output in the order of T 6  and T 7 . In this way, the conversion process into application data can be done. With this process, the vectorized object can be reused by existing document creation application software. 
   Details of a process of storing a vector image which matches a search result will be described next.  FIG. 14  is a block diagram showing the detailed arrangement of the data processing device  115  in the MFP  100  according to this embodiment. As shown in  FIG. 14 , the data processing device  115  comprises a CPU  1401 . Various kinds of operations are performed in accordance with a program stored in a ROM  1402 . 
   Referring to  FIG. 14 , a DRAM  1403  necessary for the operation is used as a work memory necessary for operating the program or an image memory to store images. An SRAM  1404  stores data to be backed up. An image conversion processing unit  1405  executes conversion from multilevel data to binary data or from binary data to multilevel data. The image conversion processing unit  1405  receives image data from the DRAM  1403  through a data bus  1416  and writes back the converted image data in the DRAM  1403 . Reference numeral  1406  denotes a rotation processing unit;  1407 , a scaling processing unit; and  1408 , a color space conversion processing unit. 
   Network I/Fs  1409  and  1410  are connected to the network I/F  114  and FAX I/F  117 , respectively. A display unit I/F  1411  is connected to the display device  116 . An input unit I/F  1412  is connected to the input device  113 . A scanner I/F  1413  is connected to the image reading device  110 . A printer I/F  1414  is connected to the printing device  112 . An HDD I/F  1415  is connected to the storage device  111 . 
   An image read by the image reading device  110  is accumulated in the DRAM  1403  through the scanner I/F  1413 . The image on the DRAM  1403  is manipulated by using the image conversion processing unit  1405 , rotation processing unit  1406 , scaling processing unit  1407 , color space conversion processing unit  1408 , and CPU  1401  serving as units to execute image processing in accordance with a mode designated by the input device  113 . The image data is transmitted from the network I/F  114  through the network I/F  1409  or printed by the printing device  112  through the printer I/F  1414 . Simultaneously, the image data is stored in the storage device  111  through the HDD I/F  1415 . 
   In addition, data from the network I/F  1409  is received and accumulated in the DRAM  1403  and storage device  111 . The image on the DRAM  1403  is converted by using the image conversion processing unit  1405 , rotation processing unit  1406 , scaling processing unit  1407 , color space conversion processing unit  1408 , and CPU  1401  serving as units to execute image processing. After that, the image is transferred to the network I/F  1409  again or transferred to the printer I/F  1414  and printed. 
   The above-described block selection (BS) process, vectorization process, and application conversion process are installed in the management PC  101  as an application program which runs in cooperation with the MFP  100 . More specifically, the above-described scan data or received data from the network I/F  117  is read out from the storage device  111  in cooperation with the MFP  100  and received through the network I/F  114 . The received image is processed by the application program. 
     FIG. 15  is a flowchart for explaining details of a vector image accumulation process in the MFP  100  according to this embodiment. The MFP  100  receives a keyword stored in the management PC  101  in advance and displays the keyword on the display device  116  (step S 1501 ). A keyword selected by using the input device  113  is transmitted to the management PC  101  (step S 1502 ). A scan operation by the image reading device  110  starts. 
   The MFP  100  scans one page and stores the image in the storage device  111  (step S 1503 ). An event representing that the data can be transmitted is transmitted to the management PC  101  (step S 1504 ). It is determined whether the management PC  101  is in a receivable state (step S 1505 ). If the management PC  101  is in the receivable state (YES in step S 1505 ), an image of one page is read out from the storage device  111  and transmitted to the management PC  101  (step S 1506 ). If the management PC  101  is not in the receivable state (NO in step S 1505 ), the process waits until the management PC  101  is set in the receivable state, and the determination in step S 1505  is continued. 
   After the image is transmitted in step S 1506 , it is determined whether an image to be scanned is present (step S 1507 ). If such an image is present (YES in step S 1507 ), the flow returns to step S 1503  to repeat the above-described operation. If no image is present (NO in step S 1507 ), the accumulation process is ended. 
     FIG. 16  is a view for explaining a detailed example of the keyword selection process in step S 1501 . The keyword is information to inhibit copy, printing, transmission, and reuse in principle and can include characters such as “banned” or “confidential” or an image or mark representing it. Referring to  FIG. 16 , reference numeral  1601  denotes a keyword input/selection window displayed on the display screen. A file or a character string serving as a keyword of an unvectorized object such as text, natural image, frame, table, or line art can be selected in this window. A check box  1602  is used to select a keyword to search for a character string such as text, title, or caption. A display box  1603  is used to display a keyword registered in the management PC  101 . Instead of selecting a keyword, it may be input by using the input device  113 . If an object serving as a keyword is a natural image, it can also be read by the image reading device  110  and registered. 
   A check box  1604  is used to select a keyword to search for a natural image. A display box  1603  is used to display a file serving as a keyword, which is stored in the management PC  101 . A check box  1607  is used for a frame. A display box  1608  is used to display a keyword file of a frame. A check box  1609  is used for a table. A display box  1610  is used to display a keyword file of a table. A check box  1611  is used for a line art. A display box  1612  is used to display a keyword file of a line art. 
   A box  1613  is used to input a similarity. If the similarity is equal to or more than the value displayed in the box  1613 , it is regarded that two keywords match. A button  1614  is used to display a preview of the above-described selected file. An OK button  1615  is used to validate setting. 
     FIG. 17  is a flowchart for explaining operation procedures of the management PC  101  to save vector data. The management PC  101  receives a search keyword to store a vectorized image which is transmitted from the MFP  100  in step S 1507  in  FIG. 15  described above (step S 1701 ). The management PC  101  continues the standby state until it is determined that the search keyword is received (step S 1702 ). 
   After the search keyword is received, it is determined whether the management PC  101  is in an image receivable state (step S 1703 ). If the management PC  101  is in the image receivable state (YES in step S 1703 ), the management PC  101  receives page data transmitted from the MFP  100  (step S 1704 ). The block selection process and vectorization process are executed for the received page data (step S 1705 ). The similarity to the keyword received from the MFP  100  is checked (step S 1706 ). 
   If the similarity falls within a set range (e.g., equal to or more than a set numerical value) (YES in step S 1706 ), the vector data is saved (step S 1707 ). If the similarity does not satisfy the set value (NO in step S 1706 ), the generated vector data is discarded (step S 1708 ). 
     FIG. 18  is a flowchart for explaining keyword generation procedures of the image processing system according to this embodiment. The MFP  100  scans a document (step S 1801 ). The raster image obtained by scanning by the MFP  100  is transmitted to the management PC  101  (step S 1802 ). The management PC  101  receives vectorized information from the MFP  100  (step S 1803 ) and displays it on the display device (step S 1804 ). 
   The management PC  101  causes the user to select a necessary keyword (step S 1805 ) and determines whether at least one keyword is present (step S 1806 ). If at least one keyword is present (YES in step S 1806 ), the management PC  101  is requested to save the keyword (step S 1807 ). With this process, the management PC  101  saves the requested keyword and uses it in keyword selection described in the flowchart in  FIG. 17 . If no keyword is present (NO in step S 1806 ), the process is ended. 
   An example wherein the similarity to the keyword is high in the determination process in step S 1706  will be described. In this embodiment, the similarity of a character string is determined on the basis of the number of characters which match a keyword. For example, when a character string “This is ABCD” is input in correspondence with a keyword “This is ABEF”, eight of the 10 characters match. Hence, the similarity is 80%. 
   For a natural image, the similarity can be calculated from the mean square error, as indicated by equation (1). In equation (1), Key is the pixel value of a keyword, and Image is the pixel value of a scan image. 
   
     
       
         
           
             
               
                 
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   For a table, frame, or line art, the similarity can be calculated from the number of columns or the relative length of a line which is present at a relatively coincident place. Alternatively, Image is multiplied by a ratio of a vector image as Key and the vector image of scanned Image to correct the size. After correction is done as shown in  FIG. 19 , the difference vector (Key−Image) is obtained. Then, the similarity can be calculated from the mean square error.  FIG. 19  is a view showing the difference vector between a vector image as Key and scanned Image. 
   As described above, according to the image processing system of this embodiment, only data which match keywords and have a set similarity or more remain in the management PC  101  as vector images. Hence, the image storage area to store image data can be small, and only necessary data can be left. Vector data is stored with reference to user&#39;s department management data or in correspondence with the date/time. Hence, who has stored the data and when the data is stored can easily be searched later. 
   When a predetermined text is used as an object serving as a keyword, determination can be done in accordance with a character type based on character codes. The size, color, and shape of each character are not used for determination. As compared to a process of comparing image patterns, the determination process can greatly be simplified. In addition, the memory to register the image patterns can also efficiently be used. 
   When graphic information converted into a function is used as a keyword, determination can be done on the basis of the feature of the function (the size and color of a graphic are not used for determination). Hence, the same effect as in text can be obtained. 
   When it is determined that at least one of objects contained in scanned image information is similar to input keyword information, not only the object portion but also vector data generated from the image information may directly be stored in the storage device. With this arrangement, when a document with a text “secret” or the company&#39;s logotype is copied without permission, the entire document can be stored as vector data. If the storage device has an extra capacity, image information before vectorization may be left. 
   A process of scanned image information has been described above. However, the present invention is not limited to this. The present invention can be applied to various kinds of processes of, e.g., printing, transmitting, or storing, in a memory, image information received from a device on a network, image information received by facsimile, or image information read out from a memory. 
   Note that the present invention can be applied to an apparatus comprising a single device or to system constituted by a plurality of devices. 
   Furthermore, the invention can be implemented by supplying a software program, which implements the functions of the foregoing embodiments, directly or indirectly to a system or apparatus, reading the supplied program code with a computer of the system or apparatus, and then executing the program code. In this case, so long as the system or apparatus has the functions of the program, the mode of implementation need not rely upon a program. 
   Accordingly, since the functions of the present invention are implemented by computer, the program code installed in the computer also implements the present invention. In other words, the claims of the present invention also cover a computer program for the purpose of implementing the functions of the present invention. 
   In this case, so long as the system or apparatus has the functions of the program, the program may be executed in any form, such as an object code, a program executed by an interpreter, or scrip data supplied to an operating system. 
   Example of storage media that can be used for supplying the program are a floppy disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a CD-RW, a magnetic tape, a non-volatile type memory card, a ROM, and a DVD (DVD-ROM and a DVD-R). 
   As for the method of supplying the program, a client computer can be connected to a website on the Internet using a browser of the client computer, and the computer program of the present invention or an automatically-installable compressed file of the program can be downloaded to a recording medium such as a hard disk. Further, the program of the present invention can be supplied by dividing the program code constituting the program into a plurality of files and downloading the files from different websites. In other words, a WWW (World Wide Web) server that downloads, to multiple users, the program files that implement the functions of the present invention by computer is also covered by the claims of the present invention. 
   It is also possible to encrypt and store the program of the present invention on a storage medium such as a CD-ROM, distribute the storage medium to users, allow users who meet certain requirements to download decryption key information from a website via the Internet, and allow these users to decrypt the encrypted program by using the key information, whereby the program is installed in the user computer. 
   Besides the cases where the aforementioned functions according to the embodiments are implemented by executing the read program by computer, an operating system or the like running on the computer may perform all or a part of the actual processing so that the functions of the foregoing embodiments can be implemented by this processing. 
   Furthermore, after the program read from the storage medium is written to a function expansion board inserted into the computer or to a memory provided in a function expansion unit connected to the computer, a CPU or the like mounted on the function expansion board or function expansion unit performs all or a part of the actual processing so that the functions of the foregoing embodiments can be implemented by this processing. 
   According to the present invention, it is determined whether a vectorized input image contains predetermined object information. If the image contains the predetermined object information, the data is stored as vector data. An input image containing a predetermined object can be specified and saved as vector data. Hence, the memory of the system can efficiently be used. In addition, tracking from saved data can easily be done. 
   It is determined whether keyword information contained in an input image matches a predetermined keyword. If it is determined that the keyword information matches the keyword, the data is stored in correspondence with specific property information. Hence, the determination process can be facilitated as compared to a process of comparing image patterns. 
   As many apparently widely different embodiments of the present invention can be made without departing from the spirit and scope thereof, it is to be understood that the invention is not limited to the specific embodiments thereof except as defined in the appended claims. 
   CLAIM OF PRIORITY 
   This application claims priority from Japanese Patent Application No. 2004-200806 filed on Jul. 7, 2004, which is hereby incorporated by reference herein.