Patent Publication Number: US-6341176-B1

Title: Method and apparatus for character recognition

Description:
BACKGROUND OF THE INVENTION 
     (1) Field of the Invention 
     The present invention relates to a method and an apparatus for character recognition used when a document such as a printed document, a hand-written document or the like, which is not converted into text data, is converted into text data. 
     (2) Related Art 
     There is a certain type of a character recognizing apparatus for converting a printed document or a hand-written document into text data, into which a post-processing is introduced to propose a plurality of candidate characters if the apparatus cannot accurately recognize a character in the document so as to determine a correct character among the plural candidate characters, whereby a rate of recognition is improved. 
     FIG. 45 is a block diagram showing a general character recognizing apparatus. Now, an operation of the general character recognizing apparatus will be described with reference to FIG.  45 . An image inputting unit  10  captures a paper document, and converts it into image data in a form of bit map. A region dividing unit  31  divides the image data into a character region and a region of picture, graphics or the like other than the character region. 
     A character extracting unit  32  extracts one character from the divided character region, and supplies it to a character recognizing unit  33 . The character recognizing unit  33  recognizes the character to convert it into character data, and makes a plurality of conversion candidate characters. When a process of recognizing all characters in the character region is completed, a post-processing unit  34  morphologically analyzes a sentence configured with a combination of the conversion candidate characters. 
     Namely, the post-processing unit  34  requests a dictionary searching unit  20  to search for a word as a search condition. The dictionary searching unit  20  searches for the given word in a word dictionary  40 , and replies as to whether or not there is the word in the word dictionary  40 . The post-processing unit  34  outputs the word as a correct word if the word exists in the word dictionary  40 . 
     The character recognizing apparatus corrects a character improperly recognized by the character recognizing unit  33 , using the dictionary, as above. 
     However, the above character recognizing apparatus with the above structure requires enormous labor and time to make a dictionary such as the word dictionary, and maintenance thereof since the morphological analysis is carried out using the dictionary as a post-processing. 
     Further, the morphological analysis requires complex processes, a lot of time to configure and operate a system therefor, and tends to make a lot of mistakes if there exists an unrecognizable word in the document. 
     In the light of the above problems, an object of the present invention is to provide a method and an apparatus for character recognition, which can accurately correct misrecognition, and whose system can be configured readily and within a short period of time. 
     SUMMARY OF THE INVENTION 
     The object of the present invention is achieved by providing a character recognizing method, comprising the steps of: 
     recognizing an input character image indicating an input character of an input document as one or more conversion candidate characters denoting candidates for the input, character for each of input character images indicating input characters of the input document; 
     selecting a series of search character images indicating a series of search input characters from the series of input character images; 
     selecting a plurality of particular conversion candidate character strings respectively corresponding to the series of search character images from the particular conversion candidate characters; 
     preparing registered text data indicating one or more registered documents; 
     searching the registered text data for one particular conversion candidate character string for each of the particular conversion candidate character strings to count an occurrence frequency of the particular conversion candidate character string in the registered text data for each of the particular conversion candidate character strings; 
     selecting a specific particular conversion candidate character string corresponding to the highest occurrence frequency among those of the particular conversion candidate character strings from the particular conversion candidate character strings; and 
     determining a series of specific particular conversion candidate characters composing the specific particular conversion candidate character string as a series of correct characters for the series of search character images. 
     The object of the present invention is also achieved by providing a character recognizing apparatus, comprising: 
     character recognizing means for recognizing an input character image indicating an input character of an input document as one or more conversion candidate characters denoting candidates for the input character for each of input character images indicating input characters of the input document, selecting a series of search character images indicating a series of search input characters from the series of input character images and selecting a plurality of particular conversion candidate character strings respectively corresponding to the series of search character images from the particular conversion candidate characters; 
     registered text data storing means for storing registered text data indicating one or more registered documents; 
     full text searching means for searching the registered text data stored by the registered text data storing means for one particular conversion candidate character string for each of the-particular conversion candidate character strings recognized by the character recognizing means to count an occurrence frequency of the particular conversion candidate character string in the registered text data for each of the particular conversion candidate character strings; 
     post-processing means for selecting a specific particular conversion candidate character string corresponding to the highest occurrence frequency among those of the particular conversion candidate character strings counted by the full text searching means from the particular conversion candidate character strings recognized by the character recognizing means and determining a series of specific particular conversion candidate characters composing the specific particular conversion candidate character string as a series of correct characters for the series of search character images; and 
     registered text data outputting means for outputting the series of correct characters determined by the post-processing means as the series of search character images. 
     In the above steps and configuration, under circumstances where a character recognition cannot be correctly performed, an input character image indicating an input character is recognized as one or more conversion candidate characters for each of input character images indicating input characters. The conversion candidate characters denote candidates for the input character. Thereafter, a series of search character images is selected from the input character images, and a plurality of particular conversion candidate character strings respectively corresponding to the series of search character images are produced from the particular conversion candidate characters by repeatedly selecting the series of particular conversion candidate characters corresponding to the series of search character images. Thereafter, the registered text data indicating one or more registered documents is searched for each particular conversion candidate character string. Therefore, an occurrence frequency of each particular conversion candidate character string in the registered text data can be counted. Thereafter, a specific particular conversion candidate character string corresponding to the highest occurrence frequency is selected, and a series of specific particular conversion candidate characters composing the specific particular conversion candidate character string is determined as a series of correct characters. Therefore, the series of search character images can be correctly recognized as the series of correct characters. 
     Accordingly, because the invention searches registered text data for each particular conversion candidate character string to count an occurrence frequency of each particular conversion candidate character string in the registered text data and to select the specific particular conversion candidate character string corresponding to the highest occurrence frequency, it is unnecessary to prepare a dictionary such as a word dictionary requiring a lot of labor, time and maintenance, and it is unnecessary to perform a morphological analysis requiring a lot of time to configure a conventional character recognizing apparatus or perform a conventional character recognizing method in order to conduct complex processes, although tending to make a lot of mistakes. As a result, it is possible to readily configure the character recognizing apparatus, and correctly recognize characters within a short period. 
     Also, because the invention performs a full text search rather than a keyword search, segmentation of a series of search input characters is not restricted by the concept of a word, a sentence, a clause or the like, and the series of search input characters can be freely set. It is therefore possible to desirably set a speed or an accuracy of the post processing at need. 
     The object of the present invention is further achieved by the provision of a character recognizing method, comprising the steps of: 
     recognizing an input character image indicating an input character of an input document as one or more conversion candidate characters denoting candidates for the input character for each of input character images indicating input characters of the input document; 
     calculating an evaluation value indicating a degree of certainty of one conversion candidate character for each of the conversion candidate characters corresponding to the input character images; 
     selecting one or more particular conversion candidate characters corresponding to the evaluation values higher than those of the other conversion candidate characters from the conversion candidate characters corresponding to one input character image for each of the input character images; 
     selecting a series of search character images indicating a series of search input characters from the series of input character images; 
     selecting a plurality of particular conversion candidate character strings respectively corresponding to the series of search character images from the particular conversion candidate characters; 
     preparing registered text data indicating one or more registered documents; 
     searching the registered text data for one particular conversion candidate character string for each of the particular conversion candidate character strings to select a specific particular conversion candidate character string frequently occurred in the registered text data from the particular conversion candidate character strings; and 
     determining a series of specific particular conversion candidate characters composing the specific particular conversion candidate character string as a series of correct characters for the series of search character images. 
     The object of the present invention is also achieved by providing a character recognizing apparatus, comprising: 
     character recognizing means for recognizing an input character image indicating an input character of an input document as one or more conversion candidate characters denoting candidates for the input character for each of input character images indicating input characters of the input document, calculating an evaluation value indicating a degree of certainty of one conversion candidate character for each of the conversion candidate characters corresponding to the input character images, selecting one or more particular conversion candidate characters corresponding to the evaluation values higher than those of the other conversion candidate characters from the conversion candidate characters corresponding to one input character image for each of the input character images, selecting a series of search character images indicating a series of search input characters from the series of input character images and selecting a plurality of particular conversion candidate character strings respectively corresponding to the series of search character images from the particular conversion candidate characters; 
     registered text data storing means for storing registered text data indicating one or more registered documents; 
     fill text searching means for searching the registered text data stored by the registered text data storing means for one particular conversion candidate character string for each of the particular conversion candidate character strings produced by the character recognizing means to obtain a full text search result; 
     post-processing means for selecting a specific particular conversion candidate character string frequently occurred in the registered text data from the particular conversion candidate character strings according to the full text search result obtained by the full text searching means and determining a series of specific particular conversion candidate characters composing the specific particular conversion candidate character string as a series of correct characters for the series of search character images; and 
     registered text data outputting means for outputting the series of correct characters determined by the post-processing means as the series of search character images. 
     In the above steps and configuration, an evaluation value indicating a degree of certainty of one conversion candidate character is calculated for each of the conversion candidate characters, and one or more particular conversion candidate characters corresponding to the evaluation values higher than those of the other conversion candidate characters are selected from the conversion candidate characters for each of the input character images. Thereafter, the registered text data is searched for each particular conversion candidate character string, a specific particular conversion candidate character string frequently occurred in the registered text data is selected from the particular conversion candidate character strings, and a series of specific particular conversion candidate characters composing the specific particular conversion candidate character string is determined as a series of correct characters. Therefore, the series of search character images can be correctly recognized as the series of correct characters. 
     Accordingly, because the invention calculates an evaluation value of one conversion candidate character for each of the conversion candidate characters to select one or more particular conversion candidate characters corresponding to the higher evaluation values from the conversion candidate characters for each of the input character images, the number of particular conversion candidate character strings can be reduced, and a time required for a full text searching operation can be reduced. Also, because a specific particular conversion candidate character string frequently occurred in the registered text data is selected from the particular conversion candidate character strings, it is unnecessary to prepare a dictionary such as a word dictionary requiring a significant amount of labor, time and maintenance, and it is unnecessary to perform a morphological analysis requiring a significant amount of time to configure a conventional character recognizing apparatus or perform a conventional character recognizing method in order to conduct complex processes, although tending to make a lot of mistakes. As a result, it is possible to readily configure the character recognizing apparatus, and to correct misrecognized characters within a short period. 
     Preferably, the step of searching the registered text data comprises the step of searching the registered text data and the input document for one particular conversion candidate character string for each of the particular conversion candidate character strings to count an occurrence frequency of the particular conversion candidate character string in the registered text data and the input document for each of the particular conversion candidate character strings. 
     In accordance with the invention, it is also preferred that the step of searching the registered text data comprise the step of searching the registered text data and the input document for one particular conversion candidate character string for each of the particular conversion candidate character strings to select a specific particular conversion candidate character string frequently occurred in the registered text data and the input document from the particular conversion candidate character strings. 
     In accordance with the invention, it is further preferred that the step of searching the registered text data comprise the steps of: 
     searching the registered text data for one particular conversion candidate character string for each of the particular conversion candidate character strings to count a first occurrence frequency of the particular conversion candidate character string in the registered text data for each of the particular conversion candidate character strings; 
     determining a threshold value lower than the highest first occurrence frequency by a prescribed value; 
     selecting one or more first selected conversion candidate character strings corresponding to the first occurrence frequencies equal to or higher than the threshold value among those of the particular conversion candidate character strings from the particular conversion candidate character strings; 
     searching the input document for one first selected conversion candidate character string for each of the first selected conversion candidate character strings to count a second occurrence frequency of the first selected conversion candidate character string in the input document for each of the first selected conversion candidate character strings; and 
     selecting a specific particular conversion candidate character string corresponding to the highest second occurrence frequency among those of the first selected conversion candidate character strings from the first selected conversion candidate character strings. 
     In the above steps, because the input document itself is used for a full text searching operation, tendency of the input document such as words, grammar and the like used in the input document can be reflected upon correctly recognizing characters, and an unregistered word not registered in any registered documents can be searched since the unregistered word very likely appears in its own document. Therefore, a rate of character recognition can be improved. 
     In accordance with the invention, it is preferred that the step of recognizing an input character image include the steps of: 
     determining a character image position of each input character image in the input document; and 
     extracting each input character image from the input document according to the character image position, and that the step of calculating an evaluation value include the steps of: 
     again determining a second character image position of the input character image in cases where all evaluation values of the conversion candidate characters corresponding to the input character image supposed to be placed at the first character image position are lower than a threshold value and again extracting the input character image supposed to be placed at the second character image position from the input document, 
     again recognizing the input character image placed at the second character image position as one or more conversion candidate characters; and 
     again calculating an evaluation value of each conversion candidate character corresponding to the input character image placed at the second character image position. 
     In the above steps, in cases where all evaluation values of the conversion candidate characters corresponding to the input character image placed at the second character image position are lower than a threshold value, it is judged that the input character image placed at the second character image position is extracted from the input document according to the character image position not correctly indicating a position thereof. Therefore, the input character image placed at the second character image position is again extracted from the input document according to the character image position thereof, is recognized as one or more conversion candidate characters, and an evaluation value of each conversion candidate character is again calculated. 
     Accordingly, even though an input character image is incorrectly extracted from the input document, the input character image can be again extracted from the input document, so that a rate of character recognition can be improved. 
     In accordance with the invention, it is preferred that: 
     the step of recognizing an input character image includes the steps of specifying a plurality of character regions existing in the input document; and 
     extracting each of the input character images from the character regions; that 
     the step of selecting a series of search character images includes the step of combining one or a series of particular input character images extracted from a final portion of one character region and one or a series of particular input character images extracted from a top portion of another character region into the series of search character images, for each pair of character regions, and that 
     the step of determining a series of specific particular conversion candidate characters includes the step of coupling a first character region and a second character region together in that order, in cases where one particular conversion candidate character string corresponding to one series of search character images obtained by combining one or a series of particular input character images extracted from a final portion of the first character region and one or a series of particular input character images extracted from a top portion of the second character region is selected as one specific particular conversion candidate character string, for each specific particular conversion candidate character string. 
     In accordance with the invention, it is also preferred that the character recognizing apparatus further comprise: 
     character extracting means for specifying a plurality of character regions existing in the input document and extracting each of the input character images from the character regions, wherein one or a series of particular input character images extracted from a final portion of one character region and one or a series of particular input character images extracted from a top portion of another character region are combined into the series of search character images by the character recognizing means, for each pair of character regions; and 
     region coupling means for coupling a first character region and a second character region extracted by the character extracting means together in that order, in cases where one particular conversion candidate character string corresponding to one series of search character images obtained by combining one or a series of particular input character images extracted from a final portion of the first character region and one or a series of particular input character images extracted from a top portion of the second character region is selected as one specific particular conversion candidate character string by the post-processing means, for each specific particular conversion candidate character string. 
     In the above steps and configuration, even though a character area of the input document is divided into a plurality of character regions, because a first character region and a second character region are coupled together in that order in cases where one particular conversion candidate character string corresponding to one series of search character images obtained by combining one or a series of particular input character images extracted from a final portion of the first character region and one or a series of particular input character images extracted from a top portion of the second character region is selected as one specific particular conversion candidate character string, the character regions can be coupled together in a correct order. 
     In accordance with the invention, it is preferred that the step of searching the registered text data comprise the steps of 
     selecting a plurality of shortened conversion candidate character strings corresponding to a series of search character images from the particular conversion candidate character strings; 
     searching the registered text data for one shortened conversion candidate character string for each of the shortened conversion candidate character strings to count an occurrence frequency of the shortened conversion candidate character string in the registered text data for each of the shortened conversion candidate character strings; 
     selecting a specific shortened conversion candidate character string corresponding to the highest occurrence frequency along those of the shortened conversion candidate character strings from the shortened conversion candidate character strings; 
     producing a plurality of particular conversion candidate character strings respectively including the specific shortened conversion candidate character string and corresponding to the series of search character images; and 
     searching the registered text data for each particular conversion candidate character string to count an occurrence frequency of each particular conversion candidate character string in the registered text data. 
     In the above steps, because the invention permits a full text searching operation for each shortened conversion candidate character string to be performed before a full text searching operation for each particular conversion candidate character string is performed, a time required for the full text searching operation can be shortened, and characters can be correctly recognized at a short time. 
     In accordance with the invention, it is preferred that the step of recognizing an input character image include the step of specifying an input attribute of the input document, that 
     the step of preparing registered text data includes the step of classifying the registered text data into the plurality of registered documents respectively specified by a registered attribute, and that 
     the step of searching the registered text data comprises the steps of selecting one or more particular registered documents respectively specified by the registered attribute, which is the same as the input attribute of the input document, from the registered documents; and searching the particular registered documents for one particular conversion candidate character string for each of the particular conversion candidate character strings to count an occurrence frequency of the particular conversion candidate character string in the registered text data for each of the particular conversion candidate character strings. 
     In accordance with the invention, it is also preferred that the step of recognizing an input character image include the step of specifying an input attribute of the input document, that 
     the step of preparing registered text data includes the step of classifying the registered text data into the plurality of registered documents respectively specified by a registered attribute, and that 
     the step of searching the registered text data comprises the steps of 
     selecting one or more particular registered documents respectively specified by the registered attribute, which is the same as the input attribute of the input document, from the registered documents; and 
     searching the particular registered documents for one particular conversion candidate character string for each of the particular conversion candidate character strings to select a specific particular conversion candidate character string frequently occurred in the registered text data from the particular conversion candidate character strings. 
     In the above steps, because the invention searches particular registered documents, having the same attribute as that of the input document, for each particular conversion candidate character string, a character recognition reflecting upon an attribute of the input document can be performed, and the character recognition can be more correctly performed. 
     It is preferred that the step of preparing registered text data include the step of preparing pieces of misrecognition data respectively composed of a misrecognized character string including a misrecognized character and a correct character string made of a plurality of correct characters, and that 
     the step of searching the registered text data comprise the steps of searching the misrecognized character strings of the pieces of misrecognition data for one particular conversion candidate character string for each of the particular conversion candidate character strings; 
     recognizing the series of search character images as a series of correct characters composing a correct character string corresponding to one particular conversion candidate character string in the pieces of misrecognition data in cases where the particular conversion candidate character string exists in the misrecognized character strings; and 
     searching the registered text data for one particular conversion candidate character string for each of the particular conversion candidate character strings, in cases where any particular conversion candidate character string does not exist in the misrecognized character strings, to count an occurrence frequency of the particular conversion candidate character string in the registered text data for each of the particular conversion candidate character strings. 
     In accordance with the invention, it is also preferred that the step of preparing registered text data include the step of preparing pieces of misrecognition data respectively composed of a misrecognized character string including a misrecognized character and a correct character string made of a plurality of correct characters, and that 
     the step of searching the registered text data comprise the steps of searching the misrecognized character strings of the pieces of misrecognition data for one particular conversion candidate character string for each of the particular conversion candidate character strings; 
     recognizing the series of search character images as a series of correct characters composing a correct character string corresponding to one particular conversion candidate character string in the pieces of misrecognition data in cases where the particular conversion candidate character string exists in the misrecognized character strings; and 
     searching the registered text data for one particular conversion candidate character string for each of the particular conversion candidate character strings, in cases where any particular conversion candidate character string does not exist in the misrecognized character strings, to select a specific particular conversion candidate character string frequently occurred in the registered text data from the particular conversion candidate character strings. 
     In the above steps, the misrecognized character strings of the pieces of misrecognition data are searched for each particular conversion candidate character string. In cases where one particular conversion candidate character string exists in the misrecognized character strings, the series of search character images is recognized as a series of correct characters composing a correct character string corresponding to the particular conversion candidate character string in the pieces of misrecognition data. 
     Accordingly, because the invention does not require a searching operation for the registered text data in cases where one particular conversion candidate character string exists in the misrecognized character strings, character recognition can be performed at a short time. 
     In accordance with the invention, it is preferred that the character recognizing apparatus further comprise 
     layout storing means for storing an input layout of the input character images of the input document recognized by the character recognizing means; and 
     displaying means for displaying a corrected document, which is obtained by replacing the series of search character images of the input document selected by the character recognizing means with the series of correct characters determined by the post-processing means, in the input layout of the input document stored by the layout storing means. 
     In the above configuration, input character images of the input document are changed to correct characters to obtain a correct document, and the correct document is displayed in the same layout as that of the input document. 
     Accordingly, the invention makes it possible to display the correct document in a layout looking like the input document, so that the user can see easily the correct document. In addition, because image data is not merely displayed but text data is displayed, it is possible to easily edit the correct document. 
     In accordance with the invention, it is preferred that the step of a series of search character images comprises the steps of detecting a series of particular input character images sandwiched by a pair of partition symbols from the series of input character images of the input document the input document; and 
     setting the series of particular input character images as the series of search character images indicating the series of search input characters. 
     In the above steps, because a series of particular input character images sandwiched by a pair of partition symbols is set as the series of search character images, the series of search input characters indicated by the series of particular input character images has a meaning. 
     Accordingly, the invention makes it possible to recognize characters according to characteristics of a language used in the input document while avoiding a meaningless character string which is a part of a word. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a block diagram showing a structure of a character recognizing apparatus according to a first embodiment of this invention; 
     FIG. 2 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the first embodiment; 
     FIG. 3 shows image data used in the first embodiment; 
     FIG. 4 illustrates a process of extracting a character according to the first embodiment; 
     FIG. 5 shows a data structure of results of character recognition according to the first embodiment; 
     FIG. 6 shows an example of results of the character recognition according to the first embodiment; 
     FIG. 7 shows an example of results of a misrecognized character determining process according to the first embodiment; 
     FIG. 8 shows an example of results of a full text search according to the first embodiment; 
     FIG. 9 is a flowchart illustrating a process of determining a misrecognized character according to the first embodiment; 
     FIG. 10 is a flowchart illustrating a process of correcting a misrecognized character according to the first embodiment; 
     FIG. 11 is a flowchart illustrating a registering process conducted by a full text searching unit of the character recognizing apparatus according to the first embodiment; 
     FIG. 12 is a block diagram showing a structure of a character recognizing apparatus according to a second embodiment of this invention; 
     FIG. 13 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the second embodiment; 
     FIG. 14 is a flowchart illustrating a process of correcting a misrecognized character according to the second embodiment; 
     FIG. 15 shows results of the full text search according to the second embodiment; 
     FIG. 16 is a block diagram showing a structure of a character recognizing apparatus according to a third embodiment of this invention; 
     FIG. 17 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the third embodiment; 
     FIG. 18 is a flowchart illustrating a process of correcting a misrecognized character according to the third embodiment; 
     FIG. 19 shows a data structure of position information of characters according to the third embodiment; 
     FIG. 20 is a block diagram showing a structure of a character recognizing apparatus according to a fourth embodiment of this invention; 
     FIG. 21 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the fourth embodiment; 
     FIG. 22 shows an example of image data of a document used in the fourth embodiment; 
     FIGS. 23A through 23C illustrate a process of coupling regions of the image data of the document according to the fourth embodiment; 
     FIG. 24 is a block diagram showing a structure of a character recognizing apparatus according to a fifth embodiment of this invention; 
     FIG. 25 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the fifth embodiment; 
     FIG. 26 shows a data structure of results of a character recognizing process according to the fifth embodiment; 
     FIG. 27 shows an example of results of the character recognizing process according to the fifth embodiment; 
     FIGS. 28A through 28C show an example of results of the full text search according to the fifth embodiment; 
     FIG. 29 is a block diagram showing a structure of a character recognizing apparatus according to a sixth embodiment of this invention; 
     FIG. 30 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the sixth embodiment; 
     FIG. 31 shows a data structure of data in a text storing unit in the character recognizing apparatus according to the sixth embodiment; 
     FIG. 32 is a block diagram showing a structure of a character recognizing apparatus according to a seventh embodiment of this invention; 
     FIG. 33 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the sixth embodiment; 
     FIG. 34 is a flowchart illustrating a process of correcting a misrecognized character according to the sixth embodiment; 
     FIG. 35 shows a data structure of misrecognition data according to the sixth embodiment; 
     FIG. 36 is a block diagram showing a structure of a character recognizing apparatus according to an eighth embodiment of this invention; 
     FIG. 37 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the eighth embodiment; 
     FIG. 38 shows an example of image data used in the eighth embodiment; 
     FIG. 39 shows a data structure of position information about regions according to the eighth embodiment; 
     FIG. 40 shows a data structure of position information and font information of characters according to the eighth embodiment; 
     FIG. 41 is a block diagram showing a structure of a character recognizing apparatus according to a ninth embodiment of this invention; 
     FIG. 42 is a flowchart illustrating a whole process conducted by the character recognizing apparatus according to the ninth embodiment; 
     FIG. 43 shows an example of a printed document used in the ninth embodiment; 
     FIG. 44 shows an example of making a search condition according to the ninth embodiment; and 
     FIG. 45 is a block diagram showing a structure of a general character recognizing apparatus. 
    
    
     DESCRIPTION OF PREFERRED EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings, wherein like reference characters designate like or corresponding parts throughout several views. 
     (a) Description of First Embodiment 
     Now, a first embodiment of this invention will be described with reference to FIGS. 1 through 11. 
     FIG. 1 is a block diagram showing a character recognizing apparatus according to the first embodiment of this invention. 
     In FIG. 1, reference numeral  110  denotes an image inputting unit such as a scanner, facsimile or the like for inputting a printed document, or a hand-written document as image data,  120  an image data storing unit for storing the image data read by the image inputting unit  110  in a form of bit map,  170  a terminal from which a user of the character recognizing apparatus inputs necessary information, or the apparatus outputs information,  130  a text data converting unit for converting the image data stored in the image data storing unit  120  into text data,  140  a full text searching unit having a searching unit for searching for a given character string as a search condition in full text of registered data in response to a request from the text data converting unit  130  and sending back the number of times of occurrence of the search condition in the entire data, and a registering unit for making an index file from document data having been converted into the text data and storing the document data in a form of file,  150  an index file storing unit for storing the index file made by the full text searching unit  140 ,  180  a text data storing unit for storing the document data registered in the full text searching unit  140  in a form of file, and  160  a text data outputting unit for outputting the document data having been converted into the text data. 
     The text data converting unit  130  includes a region dividing unit  131  for dividing the image data into regions having different attributes (character region, graphic region, photograph region, etc.), a character extracting unit  132  for extracting a character of the image data one by one from the image data in the character region among the regions divided by the region dividing unit  131 , a character recognizing unit  133  for converting the image data extracted as the above character together with a numeral value indicating a degree of certainty of that image data, a post-processing unit  134  for correcting the misrecognition of the text data including misrecognition, which has been converted into the character data by the character recognizing unit  133 , and a recognition result storing unit  135  for storing a result of the recognition obtained by the character recognizing unit  133 . 
     Next, an operation of the character recognizing apparatus with the above structure will be described with reference to FIGS. 2 through 11. FIG. 2 is a flowchart for illustrating a flow of a whole process conducted by the character recognizing apparatus according to this embodiment, in the case where a printed document is used. 
     Assuming here that the printed document is of a size readable by the image inputting unit  110  in one operation, besides a character region in the printed document is not separated as shown in FIG.  3 . 
     By the image inputting unit  110 , the printed document is inputted as image data. After that, the inputted image data is given a unique name and stored in a form of file in the image data storing unit  120 . Here, the file is given a name, for example, bunsyo 1 .bmp (Step A 1 ). 
     Next, the user selects the image data file bunsyo 1 .bmp through the terminal  170 , and gives an instruction to convert the image data into text data. Responsive to that, the text data converting unit  130  converts the designated image data into text data. 
     The text data converting unit  130  obtains the image data file (busho 1 .bmp) designated by the user from the image data storing unit  120 . The region dividing unit  131  then divides the image data into different regions such as a photograph region, a graphic region, a character region and the like (Step A 2 ), and sends position information of the character region to the character extracting unit  132 . 
     The character extracting unit  132  determines a region of one character from, for example, spaces in the front and the rear of that character and extracts the character one by one from the beginning of the character region, and supplies information about a position of each of the characters to the character recognizing unit  133 . For instance, the character extracting unit  132  extracts a region of a character of as shown in FIG. 4, and supplies its position to the character recognizing unit  133  (Step A 3 ). 
     The character recognizing unit  133  recognizes a character obtained from the position information supplied from the character extracting unit  132 , and converts the character into character data. The character recognizing unit  133  generates a plurality of candidates for the character as a result of the recognition to make a conversion candidate characters. The character recognizing unit  133  expresses a degree of certainty of each of the conversion candidate characters by a numerical value (called an evaluation value), obtains evaluation values of all the conversion candidate characters, and outputs the conversion candidates in the order of the evaluation value. Conversion candidate characters and their respective evaluation values are stored in a form shown in FIG. 5 in the recognition result storing unit  135  (Step A 4 ). Incidentally, in the drawings, M represents the number of characters in the character region, and N a maximum number of conversion candidate characters. The evaluation value can be calculated using a degree of analogy to a reference pattern, for example. Up to this process, determination of a character string shown in FIG. 4 results in as shown in FIG. 6, for example. 
     In a similar manner, the character extracting unit  132  conducts a similar process on the next character. The processes conducted by the character extracting unit  132  and the character recognizing unit  133  are repeated up to the last character in the character region (Step A 5 ). 
     The post-processing unit  134  determines whether or not a conversion candidate  1  of the first character is misrecognized, and repeats this process on all characters of the text data stored in the recognition result storing unit  135 . 
     FIG. 9 is a flowchart showing a flow of a process of determining misrecognition. Incidentally, in the drawings, i denotes a count of conversion candidate characters, j denotes a count of characters in the character region. The determination of misrecognition is carried out as follows. First, an evaluation value of the conversion candidate  1  is compared with an evaluation value of the conversion candidate  2 , then the evaluation value of the conversion candidate  2  is compared with an evaluation value of the conversion candidate  3  and so on. If a difference d between the evaluation value of the conversion candidate  1  and the evaluation value of the conversion candidate  2  is below a threshold value, it is determined that the conversion candidate  2  is quite analogous to the conversion candidate  1 , and the conversion candidate  2  is assigned as an upper conversion candidate character since the candidate character  2  is of a high probability that the candidate character  2  is correct. 
     At this time, the conversion candidate  1  having the upper conversion candidate character is judged as a misrecognized character. According to this method, a character in a circle is determined as an upper conversion candidate character and a character in a triangle is determined as a misrecognized character in FIG. 7, if the threshold value is set to 0.4 in the example shown in FIG.  6 . 
     The present invention does not specify a method of judging candidate characters using a misrecognized character and upper conversion candidate characters. Therefore, it is alternatively possible that if there are a plurality of conversion candidate characters whose evaluation value are above a certain threshold value, all the conversion candidate characters are assigned as the upper conversion candidate character (Step A 6 ). 
     FIG. 10 is a flowchart showing a flow of a process of correcting a misrecognized character. The post-processing unit  134  requests the full text searching unit  140  to search for a character string of three characters consisting of a character judged as a misrecognized character and characters in the front and the in the rear of that character as a search condition. Further, the post-processing unit  140  replaces the misrecognized character with an upper conversion candidate character thereof in the character string, and requests the full text searching unit  140  to perform a search. If the upper conversion candidate character is plural, the post-processing unit  140  requests the full text searching unit  140  to search for each of all search conditions including respective upper conversion candidate characters. 
     The full text searching unit  140  performs a full text search in registered data stored in the text data storing unit  180  using the index files stored in the index file storing unit  150 , and sends back the number of times of occurrence of the search condition character string in all the registered data as a result of the search to the post-processing unit  134 . Assuming here that a lot of document data converted into text data by the full text searching unit  140  have been registered in the text data storing unit  180 . 
     The post-processing unit  134  compares the numbers of times of occurrence of the three-character strings obtained as results of the full text search with each other to determine that a character string having most frequently occurred has the highest probability that the character string is correct, thereby correcting the misrecognized character with that the conversion candidate character assigned in the search condition character string is assumed to be correct. 
     An example of a manner of making the search condition for the full text search is shown in FIG.  8 . In FIG. 8, when  [search character string in the first search (conversion candidate  1 )] is compared with  [search character string in the second search (conversion candidate  2 )],  has a larger value ( 400 ) of a result of the search so that  is determined to be correct (Step A 7 ). 
     As the manner of setting the search condition for the full text search, it is possible to variously set the search condition according to a characteristic of a document which is an object of character recognition. For instance, the number of characters included in the search condition may be changed, a character string interposed between two punctuation marks may be set as the search condition, a character string divided at which a different kind of character, for example, Japanese Hiragana Character, Japanese Katakana character, Kanji character or the like, appears may be assigned as the search condition. 
     The post-processing unit  134  makes a search character string for each misrecognized character to correct the misrecognized character, as stated above. As the registered data, chain information of characters which can be a word is stored so as to configure a knowledge database if the above full text searching unit is applied. 
     Text data in which correction of misrecognized characters is completed is given a unique name such as bunsho 1 .txt, and registered in the full text searching unit  140 . At this time, if an extension .bmp given by the image inputting unit  110  is changed into. txt, it is possible to correspond the image data to the text data. The text data bunsho 1 .txt is sent to the text data outputting unit  160 . 
     The full text searching unit  140  stores the text data bunsho 1 .txt in the text data storing unit  180 , besides making an index file from bunsho 1 .txt. FIG. 11 shows a flow of the registering process. The index file is stored in the index file storing unit  150 , and used as the knowledge database by the post-processing unit  134  from the next data processing (Step A 8 ). 
     The text data outputting unit  160  outputs the text data bunshol.text to a display, a facsimile or the like (Step A 9 ). 
     The character recognizing apparatus according to this embodiment replaces a character which is difficult to be recognized with each of conversion candidate characters to make a search character string, performs the full text search in plural documents which have been converted into text data to find out a correct character among the conversion candidate characters. It is therefore unnecessary to make a dictionary such as a word dictionary requiring a lot of labor and time to make the same, and maintenance thereof, and morphological analysis requiring a lot of time to configure or operate a system therefor in order to conduct complex processes, although tending to make a lot of mistakes. As a result, it is possible to readily configure the system, and correct misrecognized characters within a short period. 
     Further, the index file of a document converted into text data is made using the full text searching unit  140  one after another, and stored in the index file storing unit  150  so that the knowledge database is automatically enriched even without maintenance. 
     In particular, most of recent documents have been already converted into text data. If it is desired to convert past paper documents into text data, text data already held is registered in the full text searching unit  140 , and character recognition is conducted, going back to the past, whereby it is prevented to misrecognize a character because of occurrence of an unregistered word. 
     Since the character recognizing apparatus of this embodiment performs, not a keyword search, but a full text search, segmentation of a search character string is not restricted by concept of word, sentence, clause or the like, the search character string can be thus freely set. It is therefore possible to desirably set a speed or an accuracy of the post-processing at need. 
     According to this embodiment, an index file is made from converted text data to be used as an object of the full text search so that it is possible to manage the documents at the same time. 
     Meanwhile, the manner of the full text search is not limited to what uses the index file. 
     Moreover, it is possible to configure the system readily and within a short period by using an existing full text searching apparatus. 
     (b) Description of Second Embodiment 
     Now, description will be made of a second embodiment of this invention with reference to FIGS. 12 through 15. FIG. 12 is a block diagram showing a structure of a character recognizing apparatus according to the second embodiment. The character recognizing apparatus has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  230  including a region dividing unit  131 , a character extracting unit  132 , a character recognizing unit  133 , a post-processing unit  231  and a recognition result storing unit  135 , a terminal  170 , a full text searching unit  240 , a text data storing unit  180 , an index file storing unit  250 , and a text data outputting unit  160 , which are similar to those of the character recognizing apparatus according to the first. embodiment shown in FIG.  1 . However, the second embodiment differs from the first embodiment in that when a misrecognized character is corrected by the post-processing unit  231 , an index is made from text data before the post-processing, and not only the registered data but also a document which is an object of the character recognition is also used as the knowledge data. 
     According to this embodiment, a document which is an object of the character recognition itself is also assigned as an object of the search if the same character string repeatedly appears, and most of the characters are accurately recognized although some characters are misrecognized in the document such as a hand-written document, whereby errors are corrected at a higher speed and with a higher accuracy. 
     Hereinafter, an operation of the character recognizing apparatus according to the second embodiment will be described with reference to FIG.  13 . FIG. 13 shows a flow of a whole process conducted by the apparatus, in which Steps B 1  through B 5  are similar to Steps A 1  through A 5  according to the first embodiment shown in FIG.  2 . At Step B 6  in FIG. 13, the post-processing apparatus  231  obtains results of recognition from the recognition result storing unit  135 . 
     The post-processing unit  231  sends text data obtained from the recognition result storing unit  135  to the full text searching unit  240 . The text data is hereinafter called “own document” since it is a document which is an object of the character recognition. 
     The full text searching unit  240  makes an index file configured with only conversion candidates  1  from the received text data, and stores the index file in the index file storing unit  250 , distinguishing from indexes having been registered. 
     The post-processing unit  231  then determines misrecognized characters in the text data. The process of determining the misrecognized characters is similar to that according to the first embodiment. After that, the post-processing unit  231  corrects the misrecognized characters. 
     FIG. 14 shows a flow of the process of correcting the misrecognized character. First, the post-processing unit  231  makes a three-character string consisting of a character determined as a misrecognized character and characters in the front and in the rear of that character as the search condition, then requests the full text searching unit  240  to perform the full text search in its own document under the above search condition. The full test searching unit  240  performs the whole text search in its own document using the index file stored in the index file storing unit  250 , and sends back the number of times of occurrence of the search condition character string in its own document as a result of the search to the post-processing unit  231 . The post-processing unit  231  holds the result as a result  1 . 
     Next, the post-processing unit  231  requests the full text searching unit to search for the same search condition in the registered data. The full text searching unit  240  performs the full text search in the registered data stored in the text data storing unit  180  using the index files stored in the index file storing unit  250 , and sends back the number of times of occurrence of the search condition character string in the all registered data as a result of the search to the post-processing unit  231 . 
     Assuming here that a lot of document data converted into the text data by the full text searching unit  240  have been registered in the text data storing unit  180 . 
     The post-processing unit  231  holds the result as a result  2 . The post-processing unit  231  replaces the misrecognized character in the search condition character string with an upper conversion candidate character, and requests the full text searching unit  240  to search for the search condition character string in the above two kinds of texts in the similar manner. When the upper conversion candidate character is plural, each of all the search condition character strings including every upper conversion candidate character is searched. 
     The post-processing unit  231  compares results  2  obtained by the search with each other. A character string having occurred most frequently has the highest probability to be correct, so that a conversion candidate character assigned in the search condition character string having occurred most frequently is assumed to be a correct character, whereby the misrecognized character is corrected. 
     When a difference between the numbers of times of occurrence of the search character strings in which the conversion candidate  1  is applied and another search character string is below a certain threshold value, the results  1  of the search for these character strings is referred to, and a character string having occurred most frequently is assumed to be correct. 
     For instance, when search character strings including conversion candidate characters shown in FIG. 7 is searched in the above manner, the search results in as shown in FIG. 15, for example. As shown in FIG. 15, if the threshold value of the difference in the number of time of occurrence is  10 , it is impossible to determine a correct search character string by referring to only the results  2 . More specifically, when the results  2  of the search are compared, a difference between a result  2  ( 98 ) of the conversion candidate  1  and a result  2  ( 89 ) of the conversion candidate  2  is smaller than the threshold value ( 10 ), and a difference between the result  2  ( 98 ) of the conversion candidate  1  and a result  2  ( 107 ) of the conversion candidate  3  is smaller than the threshold value ( 10 ), so that the result  2  are not helpful in determining which is a correct search character string among the search character strings  1 ,  2  and  3 . For this, by referring to the results  1 , it is found that the conversion candidate search character string  has occurred most frequently in its own document so that  is assumed to be correct. The following processes are similar to those according to the first embodiment. 
     According to this embodiment, a document which is grammatically uniformed, that is, a document in which the same word frequently appears, or the same vein or the same expression are repeatedly used, is also used as the knowledge database so as to be an object of the search in the document itself. Accordingly, tendency of the document such as words, grammar and the like used in the document can be reflected upon correcting misrecognized characters. Moreover, an unregistered word can be searched since such unregistered word very likely appears in its own document, whereby a rate of recognition is improved. 
     In this embodiment, the search condition character string is of three characters. However, it is alternatively possible to set any number of characters as the search condition character string, not limiting to three characters. 
     (c) Description of Third Embodiment 
     Now, description will be made of a third embodiment of the present invention with reference to FIGS. 16 through 19. The third embodiment differs from the first embodiment in that when the numbers of times of occurrence of the conversion candidate character strings for correcting a misrecognized character, which are results of the full text search performed by the full text searching unit, are uniformly small upon correcting the misrecognized character, the post-processing unit decides that an extracting position of the character of image data is improper, and instructs the character extracting unit to extract the character once again. 
     FIG. 16 is a block diagram showing a structure of a character recognizing apparatus according to the third embodiment of this invention. As shown in FIG. 16, the character recognizing apparatus according to the third embodiment has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  330  including a region dividing unit  131 , a character extracting unit  331 , a character recognizing unit  332 , a post-processing unit  333  and a recognition result storing unit  135 , a terminal  170 , a full text searching unit  140 , a text data storing unit  180 , an index file storing unit  150 , and a text data outputting unit  150 , which are similar to those of the character recognizing apparatus according to the first embodiment shown in FIG. 1, excepting that the text data converting unit  330  additionally has a character position storing unit  334  for storing position information of an extracted character in a form of file when the character is extracted, and the post-processing unit  333  instructs the character extracting unit  331  to extract the character once again. 
     Hereinafter, an operation of the character recognizing apparatus according to the third embodiment will be described. FIG. 17 shows a flow of a whole process conducted by the character recognizing apparatus according to the third embodiment, wherein Steps C 1  and C 2  shown in FIG. 17 are similar to Steps A 1  and A 2  according to the first embodiment shown in FIG.  2 . At Step C 3 , the character extracting unit  331  stores position information of an extracted character in a form of file when extracting the character. At this time, the file is given a name bunshol.loc, for example. As the position information, there are obtained, with the top left-hand corner of a paper as an origin ( 0 ,  0 ), a horizontal distance X 1  from the origin to the left end of a character, a horizontal distance X 2  from the origin to the right end of the character, a vertical distance Y 1  from the origin to the top end of the character and a vertical distance Y 2  from the origin to the bottom end of the character, expressed in cm or with the number of points, for example, as shown in FIG.  19 . 
     Processes at Steps C 4  through C 6  in FIG. 17 are similar to Steps A 4  through A 6  in FIG.  2 . 
     At Step C 7  in FIG. 17, a misrecognized character is corrected. A flow of the process of correcting the misrecognized character is shown in FIG. 18. A manner of making a search condition and a manner of requesting the full text search are similar to those according to the first embodiment. However, when all obtained results of the search for search conditions for a character are smaller than a predetermined threshold value, the post-processing unit  333  determines that the character is extracted at an improper position by the character extracting unit  331 , and sends a character number of the character counted from the leading character to the character extracting unit  331  to instruct the same to extract the character once again before determining a correct character by comparing the results of the search. 
     The character extracting unit  331  obtains position information of the character having the received character number from the character position storing unit  334 , appropriately shifts the position in the horizontal and vertical directions, and again stores newly obtained position information in the character position storing unit  334 , besides sending the position information to the character recognizing unit  332 . 
     The character recognizing unit  332  recognizes a character in the received character position, and sends a result of the recognition to the post-processing unit  333 . 
     The post-processing unit  333  judges the misrecognized character on the basis of the result of the character recognition supplied from the character recognizing unit  332 . If the result is still a misrecognized character, the post-processing unit  33  determines a correct character using again the full text searching unit  140 . If the result is correct, the post-processing unit  333  processes the next character. 
     When a process of correcting all misrecognized characters is completed, the following process is conducted similarly to the first embodiment. 
     According to the third embodiment of this invention, the post-processing unit  333  of the character recognizing apparatus detects misrecognition caused from that a position at which a character of image data has been extracted is improper when correcting the misrecognized character, and instructs re-extraction of the character, whereby a rate of recognition is improved. 
     In this embodiment, the search condition character string is of three characters. However, it is alternatively possible to set any number of characters in making the search condition character string, not limiting to three characters. 
     (d) Description of Fourth Embodiment 
     Now, description will be made of a fourth embodiment of this invention with reference to FIGS. 20 through 23. The fourth embodiment is characterized in that when a plurality of character regions are generated when image data is divided, a region coupling unit couples the divided plural character regions using the full text search. 
     FIG. 20 is a block diagram showing a structure of a character recognizing apparatus according to the fourth embodiment of this invention. The character recognizing apparatus shown in FIG. 20 has an image inputting means  110 , an image data storing unit  120 , a text data converting unit  430  including a region dividing unit  131 , a character extracting unit  132 , a character recognizing unit  133 , a post-processing unit  134  and a recognition result storing unit  135 , a terminal  170 , a full text searching unit  140 , a text data storing unit  180 , an index file storing unit  150  and a text data outputting unit  160 , which are similar to those of the character recognizing apparatus according to the first embodiment shown in FIG. 1, excepting that the text data converting unit  430  additionally includes a region coupling unit  431  for coupling a plurality of regions divided by the region dividing unit  131  using the full text searching unit  140 . 
     Next, an operation of the character recognizing apparatus according to the fourth embodiment with the above structure will be described. FIG. 21 is a flowchart illustrating a whole process conducted by the character recognizing apparatus of this embodiment. Assuming here that a printed document, which is of a size readable in one operation by the image inputting unit  110  and in which the character region is separated by, for example, the photograph regions, as shown in FIG. 22, is applied in this embodiment. 
     Processes at Steps D 1  through D 3  are similar to Steps A 1  through A 3  according to the first embodiment shown in FIG.  2 . At step D 4 , characters are recognized in a similar manner to the first embodiment. When results of the recognition are stored in the recognition result storing unit  135 , different names, for example, bunsho 1   13   1 .moj, bunsho 1   13   2 .moj, . . . , are given to respective character regions and the results are stored along with the respective names in the recognition result storing unit  135 . 
     Next, the post-processing unit  134  obtains files of the character regions one by one from the recognition result storing unit  135  to judge misrecognition and correct the same in all the regions in a manner similar to the first embodiment. 
     When the above judgement and correction of the misrecognition in all the character regions are completed, the post-processing unit  134  sends results of the above processes to the region coupling unit  431 . 
     The region coupling unit  431  determines which region is followed by which region, and couples these regions to form a file. If there are three divided regions  1 ,  2  and  3 , the region coupling unit  431  requests the full text searching unit  140  to search for a character string made by coupling the last character in the region  1  and the leading character in the region  2  as the search condition. 
     Further, the region coupling unit  431  connects the last character in the region  1  to the leading character in the region  3 , the last character in the region  2  to the leading character in the region  3 , and the last character in the region  2  to the leading character in the region  1  to make each of the search conditions, and requests the full text searching unit  140  to search for each of the search condition character strings. 
     The full text searching unit  140  searches for each of the requested search conditions in the registered data, and sends back the number of times of occurrence of each of the search condition character strings in the registered data as a result. The region coupling unit  431  compares the results of the search received from the full text searching unit  140 , determines that a character string having occurred most frequently is a grammatically correct character string to couple the regions. 
     In an example shown in FIGS. 23A through 23C, it is seen from the results of the search that the order of the regions are the region  1 , the region  2  and the region  3 . More specifically, as shown in FIG. 23B, when results of the search for the search conditions  and  are compared,  has a larger result than . Accordingly, the region  1  is followed by the region  2 . When results of the search for the search conditions  and  are compared,  has a larger result so that the region is followed by the regions  3 . Similarly, when result of the search conditions  and  are compared,  has a larger result so that the region  3  is considered to be followed by the region  1 . However, the region  3  ends at a punctuation mark . So that the regions are coupled in the order of  1 ,  2  and  3 , as shown in FIG.  23 C. Incidentally, document is generally such made that the reader starts reading from the top toward the bottom. Accordingly, it is alternatively possible to use position information of a region obtained when the region is divided as a judgement value since a region in a higher position is followed by a region in a lower position in the document. 
     The regions are coupled to form one file, and given a unique name bunsho 1 .txt, as above. After that, the file is registered in the full text searching unit  140 , and sent to the text data outputting unit  160 . This process is similar to that according to the first embodiment. 
     The character recognizing apparatus according to the fourth embodiment automatically couples regions when one document is divided into a plurality of regions, and determines the order of the regions. It is therefore possible to efficiently recognize characters without labor. 
     In this embodiment, there is used a document of a size readable in one operation by the image inputting unit  110 . However, it is alternatively possible to separately read one document part by part not in order, and couple results of recognition of plural groups of image data of the document read separately in a correct order. 
     In this embodiment, the search condition character string is of two characters. However, it is alternatively possible that the search condition character string is of any number of characters, not limiting to two characters. 
     This embodiment has been described by way of an example where the image data is converted into text data. However, data to be subjected to the process of correcting misrecognition may be patterned data in speech recognition or the like. 
     (e) Description of Fifth Embodiment 
     Now, description will be made of a fifth embodiment of this invention with reference to FIGS. 24 through 28. FIG. 24 is a block diagram showing a structure of a character recognizing apparatus according to the fifth embodiment of this invention. In FIG. 24, the character recognizing apparatus has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  530  including a region dividing unit  131 , a character extracting unit  132 , a character recognizing unit  533 , a post-processing unit  534  and a recognition result storing unit  535 , a terminal  170 , a whole text retrieving unit  140 , a text data storing unit  180 , an index file storing unit  150  and a text data outputting unit  160 , which are similar to those of the character recognizing apparatus shown in FIG. 1 according to the first embodiment. However, the character recognizing apparatus according to the fifth embodiment differs from the character recognizing apparatus according to the first embodiment in that image data extracted as a character is converted into at least one character data without an evaluation value by the character recognizing unit  533 . 
     Hereinafter, an operation of the character recognizing apparatus of the fifth embodiment will be described. FIG. 25 is a flowchart showing a flow of a whole process conducted by the apparatus. Steps E 1  through E 3  in FIG. 25 are similar to Steps A 1  through A 3  according to the first embodiment shown in FIG.  2 . At step E 4  in FIG. 25, the character recognizing unit  533  recognizes one character obtained from position information supplied from the character extracting unit  132 , converts the character into one character data, and stores a result of the conversion in the recognition result storing unit  535  in a form shown in FIG.  26 . The character data converted by the character recognizing unit  530  will be hereinafter referred “a conversion candidate character”. Up to this character recognizing process, a character string shown in FIG. 4, for example, results in as shown in FIG.  27 . Step E 5  in FIG. 25 is similar to Step A 5  in FIG.  2 . 
     At Step E 6  in FIG. 25, the post-processing unit  534  determines a correct recognition result from at least one conversion candidate character stored in the recognition result storing unit  535 . 
     The post-processing unit  534  combines a conversion candidate character  1  of the first character with a conversion candidate character  1  of the second character stored in the recognition result storing unit  535  to make a search condition, and requests the full text searching unit  140  to search for the search condition. Next, the post-processing unit  534  combines a conversion candidate  2  of the first character with a conversion candidate character  1  of the second character to make a search condition, and requests the full text searching unit  140  to search for it. Similarly, the post-processing unit  534  makes every combination of the conversion candidate characters  3 ,  4 ,  5 , . . . of the first and the second character to make search conditions, and request the full text searching unit  140  to search for each of the search conditions. The post-processing unit  534  eliminates search conditions never occurred or scarcely occurred by comparing results of the search. 
     The post-processing unit  534  then attaches a conversion candidate character  1  of the third character to the tail of the remaining two-character string to make a search condition, and requests the full text search unit  140  to search for it. Similarly, the post-processing unit  534  adds each of the conversion candidate characters of the third character to the two-character string to make a search condition, and requests the full text searching unit  140  to search for it. The post-processing unit  534  compares results of the search, and determines the conversion candidate character included in the search condition having most frequently occurred as a correct character. 
     Next, the post-processing unit  534  adds each of the conversion candidate characters of the fourth character to the character string consisting of the second character and the third character determined as correct to make each of search conditions, and requests the full text searching unit  140  to search for the retrieval conditions. The post-processing unit  534  compares results of the retrieval to determine a conversion candidate character included in the search condition having most frequently occurred as a correct character. In a similar manner, the post-processing unit  534  determines the following correct characters. FIGS. 28A through 28C show an example of making a search condition for the full text search. As shown in FIG. 28A, when results of the search are compared, it is seen that a numerical value “400” obtained in the first time is the largest among them so that a search character string is combined with the third character. As to the third character, a search condition is made as shown in FIG.  28 B. When results of the search shown in FIG. 28B are compared, it is seen that a numerical value “230” obtained in the first time is larger than a numerical value “2” obtained in the second time so that a search character string  is selected as correct. As to the fourth character, a search condition is made as shown in FIG.  28 C. When results of the search shown in FIG. 28C are compared, it is seen that a numerical value “46” obtained in the first time is the largest among them so that a search character string  is determined as being correct. 
     The following processes are similar to those according to the first embodiment. 
     In this embodiment, the search condition character string is of three characters, but a manner of making the search condition character string is not limited to the above. 
     According to this embodiment, it is unnecessary to make a dictionary such as a word dictionary which requires a lot of labor and time to make the same and maintenance thereof similarly to the first embodiment even if the character recognizing apparatus does not (cannot) yield an evaluation value. It is also unnecessary to conduct morphological analysis which requires a lot of time to configure and operate the system therefor since it requires complex processes, although tending to make a lot of mistakes. Therefore, it is possible to configure the system readily and within a short period, and correct misrecognition within a short period. 
     (f) Description of Sixth Embodiment 
     Now, description will be made of a sixth embodiment of this invention referring to FIGS. 29 through 31. FIG. 29 is a block diagram showing a structure of a character recognizing apparatus according to the sixth embodiment. In FIG. 29, the character recognizing apparatus according to the sixth embodiment has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  630  including a region dividing unit  131 , a character extracting unit  132 , a character recognizing unit  133 , a post processing unit  634 , a recognition result storing unit  135 , a terminal  670 , a whole text retrieving unit  640 , a text data storing unit  680 , an index file storing unit  650  and a text data outputting unit  160 , which are similar to those of the character recognizing apparatus according to the first embodiment shown in FIG. 1, excepting that the text data converting unit  630  additionally includes an attribute obtaining unit  636  for obtaining attributes as to a region divided by the region dividing unit  131 , and the post-processing unit  634  designates attributes of registered data to which the attributes have been given when making a search request to correct misrecognition. 
     Hereinafter, an operation of the character recognizing apparatus of the sixth embodiment will be described. FIG. 30 is a flowchart showing a flow of a whole process conducted by the apparatus. In FIG. 30, Steps F 1  and F 2  are similar to Steps A 1  and A 2  according to the first embodiment shown in FIG.  2 . At Step K 3  in FIG. 30, the attribute obtaining unit  636  obtains attributes of a document in a character region divided at Step K 2 . Here, the user inputs four attributes of the document, for example, a name of a writer, a date, a mode (hand-written or printed), and a kind (report or paper), through the terminal  670 . 
     Steps E 4  through E 6  in FIG. 30 are similar to Steps A 3  through A 5  in FIG.  2 . 
     At Step E 7 , the post-processing unit  634  designates the attributes of the document when requesting the full text searching unit  640  to search, and responsive to the request, the full text searching unit  640  searches in text data having the attributes designated by the post-processing unit  634 , dissimilarly to the process at Step A 6  in FIG.  2 . Other processes are similar to those according to the first embodiment. 
     FIG. 31 shows an example of a data structure of text data stored in the text data storing unit  680 . The text data storing unit  680  stores text data of documents each having four attributes as to a date, a name of writer, a mode and a kind of the document. The index file storing unit  650  stores index files each made from the text data stored in the text data storing unit  680 . 
     In the example shown in FIG. 31, if a document to be subjected to misrecognition correction was written in 1992, a name of writer of the document is Taro Matsushita, a mode of the document is printed and a kind of the document is paper, the post-processing unit  634  designates attributes of the documents, that is, 1992, Taro Matsushita, printed and paper when requesting the full text searching unit  640  to search. The full text searching unit  640  performs the full text search in only documents having the designated attributes, that is, bodies having the record number  4  and  6  in FIG.  31 . After that, the post-processing unit  634  determines correct characters in a similar manner to the first embodiment. As a manner of designating the attributes, it is possible to designate several years before and after a date when the document was written, or only a writer, for example, depending on a characteristic of a document which is an object of character recognition. 
     The document in which misrecognized characters have been corrected is registered along with its attributes in the full text searching unit  640 . The following processes are similar to those according to the first embodiment. 
     According to this embodiment, even if various documents, dates of which separates many years from each other, which have been written by a plurality of writers, for example, are mingled as database, it is possible to correct misrecognition reflecting a character of a document which is an object of character recognition since the character recognizing apparatus performs the full text search in documents having characters similar to that of the document which is the object of the character recognition. 
     Since correct characters can be obtained without searching a large volume of text data as above, it is possible to correct misrecognition within a short period. 
     Even if a large volume of documents are stored as the knowledge database, it is unnecessary to divide the knowledge database according to characteristic of the documents. Therefore, it is possible to configure the character recognizing apparatus without being conscious of the characters of the documents so that the system can be configured readily and within a short period. 
     In this embodiment, the user inputs attributes of a document which is an object of character recognition to the attribute obtaining unit  636 . However, it is alternatively possible to automatically obtain the attributes from, for example, position information of a character region. The manner of obtaining attribute is not limited to the above example. 
     (g) Description of Seventh Embodiment 
     Now, description will be made of a seventh embodiment of this invention with reference to FIGS. 32 through 35. The seventh embodiment differs from the first embodiment in that a character string including misrecognition requested to be searched and a character string including a correct character obtained by the full text search are corresponded to each other to make misrecognition data, and the misrecognition data is registered in the full text searching unit after the misrecognition is corrected, so as to be also used in correcting another misrecognition after that. 
     FIG. 32 is a block diagram showing a character recognizing apparatus according to the seventh embodiment of this invention. The character recognizing apparatus shown in FIG. 32 has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  730  including a region dividing unit  131 , a character extracting unit  132 , a character recognizing unit  133 , a post-processing unit  734  and a recognition result storing unit  135 , a terminal  170 , a whole text retrieving unit  740 , a text data storing unit  180 , an index file storing unit  150  and a text data outputting unit  160 , which are similar to those of the character recognizing apparatus according to the first embodiment shown in FIG.  2 . The character recognizing apparatus shown in FIG. 32 additionally has a misrecognition storing unit  736  in the text data converting unit  730  for registering misrecognition data made by corresponding a character string including a misrecognized character (hereinafter referred a misrecognized character string) used as a search character string when the misrecognized character is corrected by the post-processing unit  734  and a character string including a correct character (hereinafter referred a correct character string) in the full text searching unit  740 , a misrecognized data storing unit  780  for storing the misrecognized data registered in the full text searching unit  740  in a form of file, and a misrecognition index file storing unit  750  for storing an index file made from the misrecognized data by the full text searching unit  740 . 
     Hereinafter, an operation of the character recognizing apparatus with the above structure according to the seventh embodiment will be described. FIG. 33 shows a flow of a whole process conducted by the apparatus. Processes at Steps G 1  through G 5  in FIG. 33 are similar to those at Steps A 1  through A 5  according to the first embodiment shown in FIG.  2 . 
     A process at Step G 6  in FIG. 33 is shown in detail in FIG.  34 . First, a misrecognized character is determined and a search condition is made, similarly to the first embodiment. The post-processing unit  734  requests the full text searching unit  740  to search for the search condition in the misrecognized data. 
     The full text searching unit  740  searches for the search condition in misrecognition character strings of the registered misrecognition data stored in the misrecognition data storing unit  780  by using an index file stored in the misrecognition index file storing unit  750 , and if relevant data exists, the full text searching unit  740  sends back a correct character string of a record thereof as a result of the search to the post-processing unit  734 . If no relevant data exists, the full text searching unit  740  replies to the post-processing unit  734  that there is no relevant data. 
     FIG. 35 shows a data structure of the misrecognition data stored in the misrecognition data storing unit  780 . The full text searching unit  740  makes an index file from the misrecognition character strings of the misrecognition data in a manner similar to when the document data is registered according to the first embodiment. 
     When receiving a correct character string from the full text searching unit  740 , the post-processing unit  734  determines the character string as being correct. When receiving a reply that there is not relevant data, the post-processing unit  734  requests the full text searching unit  740  to search in the text data. When no correct character can be obtained from a result of the search in the misrecognition data, the post-processing unit  734  requests the full text searching unit  740  to search for all retrieval conditions in the above two kinds of data, and compares results of the search similarly to the first embodiment to determine a correct character. When a correct character is determined in any manner, the post-processing unit  734  stores both the misrecognition character string used as the search condition to determine the correct character and a correct character string in the data structure shown in FIG. 35 in the misrecognition storing unit  736 . 
     The misrecognition storing unit  736  registers the misrecognition data received from the post-processing unit  734  in the full text searching unit  740 . The following processes are similar to the first embodiment. 
     According to this embodiment, data of a misrecognized character and a corresponding correct character is stored each time the misrecognized character is corrected, and misrecognition data in the past is also used as the knowledge database, whereby correction of misrecognition reflecting characteristics of the image inputting unit  110  is possible. 
     It is also possible to obtain a correct character by searching in the misrecognition data without searching in a large volume of text data so that correction of the misrecognition within a short period becomes possible. 
     Incidentally, it is alternatively possible to give attributes to a document as described in the sixth embodiment when the misrecognition data is searched, and search in the misrecognition data by designating the attributes. 
     (h) Description of Eighth Embodiment 
     Now, description will be made of an eighth embodiment of this invention with reference to FIGS. 36 through 40. The eighth embodiment differs from the first embodiment in that document data converted into text data is displayed in a layout looking like input image data when the document data is outputted. 
     FIG. 36 is a block diagram showing a structure of a character recognizing apparatus according to the eighth embodiment of this invention. The character recognizing apparatus shown in FIG. 36 has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  830  including a region dividing unit  831 , a character extracting unit  832 , a character recognizing unit  133 , a post-processing unit  134  and a recognition result storing unit  135 , a terminal  170 , a full text searching unit  140 , a text data storing unit  180 , an index file storing unit  150  and a text data outputting unit  860 , which are similar to those of the character recognizing apparatus according to the first embodiment shown in FIG.  1 . However, the character recognizing apparatus shown in FIG. 36 additionally has, in the text data converting unit  830 , a region position storing unit  836  for storing position information of each region when a region is divided by the region dividing unit  831 , a graphic data storing unit  838  for storing data of, for example, drawings, photographs and the like other than character data, and a character font storing unit  837  for storing a font and position information of characters obtained by the character extracting unit  832 . 
     Hereinafter, an operation of the character recognizing apparatus according to the eighth embodiment will be described. FIG. 37 shows a flow of a whole process conducted by the apparatus. Here is used a printed document, a size of which is readable in one operation by the image inputting unit  110 , and in which a character region is separated from a graphic region as shown in FIG.  38 . 
     Steps H 1  and H 2  in FIG. 37 are similar to Steps A 1  and A 2  according to the first embodiment shown in FIG.  2 . 
     At Step H 3  in FIG. 37, position information of each region divided at Step H 2  is stored in the region position storing unit  836 . More concretely, there are obtained a horizontal distance X 1  and a vertical distance Y 1  from an origin at the top left-hand corner of the paper to top left-hand corner of the region, and a horizontal distance X 2  and a vertical distance from the origin to the bottom right-hand corner of the region expressed in cm. If the region is of character data, the obtained position information is labeled as NULL, whereas if the region is of graphic data, the obtained position information is given a file name of graphic data, and stored in the region position storing unit  836 . Data of, for example, a picture, a photograph or the like other than character data is given a name zul.bmp, for example, in a form of file in the picture data storing unit  838 . 
     At Step H 4 , the character extracting unit  832  extracts a character in the character region similarly to Step A 3  in FIG. 2, and stores position information of a horizontal distance X 1  and a vertical distance Y 1  from the origin at the top left-hand corner of the paper to the top left-hand corner of the character, and a horizontal distance X 2  and a vertical distance Y 2  from the origin to the bottom right-hand corner of the character expressed in cm along with a font of the character in the character font storing unit  837 , as shown in FIG. 40, for example. Processes conducted at Steps H 5  through H 7  are similar to those at Steps A 4  through A 7  in FIG.  2 . 
     At Step H 9  in FIG. 37, the text data outputting unit  860  obtains the region position information from the region position storing unit  836 , the graphic data form the graphic data storing unit  838  and the character font information from the character font storing unit  837  when outputting document data converted into text data, so arranges the graphic data and the document data on the basis of the obtained information that output data is displayed looking like the input data, and outputs the picture data and the document using characters of a size and a font similar to those of characters of the input data. 
     According to this embodiment, output data can be displayed in a layout looking like inputted image data so that the user can see easily the data. In addition, the outputted data is, not mere displayed imaged data, but text data, the user can edit the document so as to readily make a different document in a layout looking like a paper document. 
     (i) Description of Ninth Embodiment 
     Now, description of ninth embodiment of this invention will be described with reference to FIGS. 41 through 44. FIG. 41 is a block diagram showing a structure of a character recognizing apparatus according to the ninth embodiment. The character recognizing apparatus shown in FIG. 41 has an image inputting unit  110 , an image data storing unit  120 , a text data converting unit  930  including a region dividing unit  131 , a character extracting unit  132 , a character recognizing unit  133 , a post-processing unit  934  and a recognition result storing unit  135 , a terminal  170 , a full text searching unit  140 , a text data storing unit  180 , an index file storing unit  150  and a text data outputting unit  160 , which are similar to those of the character recognizing apparatus according to the first embodiment shown in FIG. 1, excepting an operation of the post-processing unit  934  in the text data converting unit  930 . 
     The ninth embodiment is characterized in that when the post-processing unit  934  corrects misrecognition of a document written in a language such as English, Hankul (Hangul) or the like other than Japanese in which a character string is written separated by a space or the like, a character string separated by a space, that is, a character string sandwiched between spaces, is used as a search character string. 
     Hereinafter, an operation of the character recognizing apparatus with the above structure according to the ninth embodiment will be described. FIG. 42 is a flowchart showing a flow of a whole operation conducted by the apparatus. Here is used a printed document in which a word is separated by spaces as done in English or Hunkul (Hngul) language. This embodiment will be described by way of an example where a document written in English language is used as shown in FIG.  43 . 
     Processes at Steps I 1  through I 6  in FIG. 41 are similar to those at Steps A 1  through A 6  according to the first embodiment shown in FIG.  2 . At Step I 7 , the post-processing unit  934  combines a misrecognized character determined at Step I 6  and characters in the front and in the rear of the misrecognized character to make a search condition, as shown in FIG.  44 . At that time, the post-processing unit  934  uses a character string including the misrecognized character sandwiched by spaces as a search condition, and requests the full text searching unit  140  to search for it. Further, the post-processing unit  934  replaces the misrecognized character with an upper conversion candidate character to make a search condition, and requests the full text searching unit  140  to search for it. When the upper conversion candidate letter is plural, the post-processing unit  934  requests the full text searching unit  140  to search for each of all search conditions including the respective upper conversion candidate letters. The following processes are similar to the first embodiment. 
     In this embodiment, spaces are used to discriminate a character string. However, it is alternatively possible to use periods, specific characters, control codes or symbols instead of spaces. This invention is not limited to the above example. 
     In this embodiment, a character string sandwiched by spaces is used as a search condition. However, it is alternatively possible to couple a plurality of character strings to use it as a search condition. 
     According to this embodiment, when misrecognition in a document written in a language such as English or Hunkul (Hangul) where a word is written separated by spaces, the character recognizing apparatus of this embodiment searches for a character string sandwiched by spaces as a search condition. Accordingly, it is possible to correct misrecognition according to characteristics of a language or a document by avoiding a meaningless character string which is a part of a word such as “harac” in a word “character”, for example, whereby correction of misrecognition according to characteristics of a language or a document is possible.