Selection agent for a symbol determination system with multiple character recognition processors

Abstract: A selection agent within a symbol determination system receives input character codes (ICCs) each with an associated confidence factor (CF) from a plurality of OCR processors. The selection agent selects the mathematically most probable character (MPC) from among the ICCs based on the relative values of the joint confidence factor (JCF) in accordance with the relationship: JCF n = 1 - (1 - CF₁) (1 - CF₂) . . . (1 - CF n ). for "N" like ICCs from N OCR processors.

Description: This invention relates to optical character recognition (OCR) systems, and more particularly to such systems having a selection apparatus and method which identifies the mathematically most probable symbol candidate from a plurality of OCR processors. Heretofore individual OCR processors were employed to recognise symbol image data in the form of bit maps of characters, as described in US Patent 5046116, and European Application No. 91311656.2 dated 16 December 1991, by the present applicant. These individual processors were not employed as an array of processors with a selection agent to collectively identify the input image. JP-A-3085691 discloses a recognition method in which a plurality of independent OCR systems outputs a list of character candidates together with associated likelihood signals. A joint likelihood signal is formed for each list portion. The invention is defined in claims 1, 25, 30 and 53. Embodiments of the present invention will now be described with reference to the accompanying drawings in which: FIGURE 1 is a block diagram of a symbol determination system showing the character selection agent 16; FIGURE 2A is a block diagram of the selection agent of Figure 1 showing the sorting circuit for arranging like candidates into groups; FIGURE 2B shows an embodiment of the data field format of the candidate data contained in the input register of Figure 2A; FIGURE 2C shows an embodiment of the data field format of the group data contained in the output register of Figure 2A; and FIGURE 3 is a block diagram of an advancing circuit for moving the candidates into the joint probability calculator by groups. The elements of the embodiments of invention are designated by two digit reference numerals. The first digit indicates the Figure in which that element is first disclosed or is primarily described. The second digit indicates like features and structures throughout the Figures. Some reference numerals are followed by a letter which indicates a sub-portion or feature of that element. SYMBOL DETERMINATION SYSTEM (Figure 1) Holistic symbol determination system 10 receives the text and/or graphic content of documents 10D via input scanner 10S into CPU 10C, and stores the resulting image in bit map memory 12M. The symbol or character cells 12C within the bit map are identified by isolation software and presented to a plurality of suitable optical character recognition (OCR) processors such as Data Exclusion Processor 14Ex and Neural Network Processor 14NN. Character selection agent 16 selects the mathematically Most Probable Character (MPC) from among the processor outputs, for storage by the CPU or for printing at output printer 10P. The bits in the character image are presented to the OCR processors and systematically retrieved and examined by each processor. The character data generated by each processor is compared to corresponding character data from a set of known or defined reference characters stored in library 15. Each OCR processor then establishes the best match character for the presented image based on which character in the reference set most resembles the presented image as examined by that processor. The best matched character output then becomes an input character code (ICC) a candidate for selection by selection agent 16. The candidates are expressed in a suitable binary code system such as ASCII code or compressed ASCII code. There are many classes of OCR processors, each employing a different approach to character recognition. Data exclusion processor 14Ex examines the spectral power of the presented image to derive line width and line count information. The spectral power is progressively reduced to exclude image data arid generate a confidence factor as described in US Patent 5046116 and European Application no. 91311656, filed by the present assignee. Neural network processor 14NN is an adaptive artificial intelligence embodiment employing matching processes inherent in biological nervous systems. Preferably the OCR processors within a particular holistic determination system are not identical or even functionally equivalent. The processors are specifically chosen from different classes to present an array of optical character recognition principles with overlapping competence profiles. The processors in the array have broad overlapping regions of high competence and narrow nonoverlapping regions of limited competence. Each OCR class of processors has a unique approach to symbol recognition with special features and capabilities. The moment and weighting OCR approach determines the distribution of the pigment forming the image, and is particularly proficient at image intensity and greyscale matters. However the moment approach does not handle the "roundness" issue as well as other OCR approaches. The edge examination OCR approach focuses on the edges of the image to resolve inside edges from outside edges. The edge approach is not particularly proficient in examining short strokes or long strokes. Other OCR approaches concern extracting line features and extracting circles. The individual competence profile of the processors complement one another to provide a well rounded collective competence profile. In most cases the OCR processors will establish the same best matched character and ICC candidate because the same image is presented to all of the processors. However, occasionally the candidates may not be the same because the original scanned image is poor or the symbol format is unusual. The candidate may be in a low-competence region for one of the processors causing the candidate to be interpreted differently by different processors. The resulting candidate conflict is resolved by examining the confidence factors. CONFIDENCE FACTORs - CFs The processors generate a Confidence Factor (CF) associated with each input character code (ICC) candidate, indicating the probable accuracy of each ICC as the correct ASCII code of the presented image. The CF values are related to the Probability (P) of the correctness of the ICC candidates. That is: Confidence Factor = K x Probability CF = K x P where CF has an arbitrary value scale, P has a minimum value approaching 0 for low CFs and a maximum value approaching 1 for high CFs, and K is a coefficient of calibration. The CF becomes identical to the P with proper calibration of the OCR processors. That is: Calibrated Confidence Factor = Probability CF cal = P Calibrating an OCR processor involves calibration testing over long term periods by scanning special calibration documents. The correlation between the test produced CF and the empirically determined probability is observed. The CF is adjusted into normalization with the empirical probability at K=1. In general, the probability P is a special case of the confidence factor CF. When calibrated at K=1, the CF is equal to the probability of correctness, and may be stated in terms of the probability of incorrectness or error "e": CF cal = P = (1 - e) where e approaches 0 for high CF. The probability of correctness plus the probability of incorrectness is always equal to unity: P + e = 1. A CF of P = .99999 (e = .00001) represents a candidate recognition rate of 99,999 correct symbols per 100,000 symbol images presented to the processor. Alternatively stated, P = .99999 represents a candidate mis-recognition rate of one symbol in 100,000, or about one mis-recognition in every 50 pages on the average as shown in Table A. MIS-RECOGNITION RATES Probability P Error e Mean Page Run Between Mis-recognitions .99 .01 0.05 .999 .001 0.5 .999,9 .000,1 5 .999,99 .000,01 50 .999,999 .000,001 500 .999,999,9 .000,000,1 5,000 Table A is based on an 8.5" x 11" page of typed material, single spaced, containing about 2,000 characters. JOINT CONFIDENCE FACTORs - JCFs When two OCR processors establish the same candidate as the ICC candidate, their Joint Confidence Factor (JCF) is greater then either of the individual CFs. The JCF is expressed by the JCF relationship: JCF 2 = 1 - (1 - CF 1 ) (1 - CF 2 ). The general JCF equation for "n" terms (n like candidates from n OCR processors) is: JCF n = 1 - (1 - CF 1 )(1 - CF 2 ) . . . (1 - CF n ). For a calibrated system where the CF is equal to the probability the JCF equation becomes the Joint Probability (JP) equation: JP 2 = 1 - (1 - P 1 ) (1 - P 2 ) The general JP equation for "n" terms (n like candidates from n OCR processors) is: JP n = 1 - (1 - P 1 )(1 - P 2 ) . . . . (1 - P n ). The JP equation may be simplified by substituting in the probability of incorrectness: JP 2 = 1 - (e 1 ) (e 2 ) where the two e terms are multiplied forming a single error product term. The general JP error equation for "n" terms (n like candidates from N OCR processors) is: JP n = 1 - (e 1 ) (e 2 ) (e 3 ) . ... . (e n ) The e product term defines the Joint Error Probability (Jep n ) and contains the candidate fidelity information: JP n = 1 - Jep n The Jep n term approaches 0 for larger CFs and higher values of n. The JP of a group of candidates is a more accurate measure of likelihood than the single Ps of the individual candidates. The mathematically most probable candidate may not be apparent from examining the single Ps. For example, Table B shows the Ps and JPs for a three processor system in which each processor provides the four most promising candidates in order of decreasing probability. O Q G C example 1st Proc 2nd Proc 3rd Proc P / e P / e P / e JP / JeP "O" .39 / .61 .43 / .53 .33 / .67 .78 / .22 "Q" .24 / .76 .52 / .48 .15 / .85 .69 / .31 "G" .25 / .75 .03 / .97 .47 / .53 .61 / .39 "C" - .02 / .98 .05 / .95 .07 / .93 "D" .12 / .88 - - .12 / .88 The mathematically most probable candidate in the above Table B - OQGC example is "O" with JP = .78. The candidates with the highest single probability are "Q" with P = .52 and "G" with P = .47. The First Processor has a region of limited competence of round symbols, and was the only processor to provide a "D" response to the input image data. The generally low Ps and JPs in the Table B example represent a poor input image situation generated from a scanned document with low quality print. Documents in general use have higher quality print and yield a more reliable symbol image. Table C (three 9s) shows a more typical OQGC example with JP = .999 and Table D (six 9s) shows an example with JP = .999,999. (three 9s) - O Q G C example 1st Proc 2nd Proc 3rd Proc P / e P / e P / e JP / JeP "O" .90 / .10 .90 / .10 .90 / .10 .999 / .001 "Q" .05 / .95 .07 / .93 .03 / .97 .143 / .857 "G" .03 / .97 .02 / .98 .04 / .96 .087 / .913 "C" .02 / .98 .01 / .99 .03 / .97 .059 / .941 (six 9s) - O Q G C example 1st Proc 2nd Proc 3rd Proc P e P e P e JP JeP "O" .990 .010 .990 .010 .990 .010 .999,999 .000,001 "Q" .005 .995 .007 .993 .003 .997 .014,929 .985,071 "G" .003 .997 .002 .998 .004 .996 .008,974 .991,026 "C" .002 .998 .001 .999 .003 .997 .005,989 .994,011 DEFAULT IMAGE MODE Scanned text images may be ambiguous due to low quality input documents and non standard formats. When a best matched character and ICC cannot be determined (or a selection is otherwise not possible) the character selection agent goes into a non-recognition default mode. The selection agent forwards a Default Image signal instructing the CPU to print the bit map of the character in place of the undetermined ASCII code. The CPU print output has a compound text format including character codes (such as eight bit ASCII) plus image data. The image data is preferably the address code identifying where the image bit map is located in the CPU memory. The image bit map may contain thousands of bits (such as 5,000 bits representing a 100 bit by 50 bit image). Image compression algorithms may be employed to reduced the memory requirements of the default image maps. The selection agent may enter the default image mode in response to low CFs generated by poor matches in the OCR processors. ICC candidates having CFs or JCFs below a threshold value may be discarded as useless for selection purposes. The poor match may be caused by non-conventional characters which are not defined in the library, or unusual fonts beyond the recognition range of the processors. Poor matches may also be caused by impairments in the scanned medium such as folds, rips, punch holes, staple tears, and specks of foreign material. Impairments in the print may contribute to poor matching through distorted fonts, faded pigment, and loss of contrast because of faded pigment or yellowing pages. CHARACTER SELECTION AGENT (Figure 2A) Character selection agent 16 sorts the ICC (input character code) candidates by character into groups of like characters, and then combines the CFs (or the Ps or the e terms) within each group to provide the group JCF (or the JP or the Jep). Input register 22 receive the ICC candidates from "N" OCR processors, each of which provide "C" levels of ICC choices. That is, the C best matches established by each OCR processor are received into the input register. In the embodiment of Figure 2A, N = 5 (with corresponding input sub-registers 22A, 22E, 22C, 22D and 22E), and C = 3 (with corresponding register positions 1st-C, 2nd-C, and 3rd-C). The number of input candidates is NxC (5x3 = 15), which require an equal number of input positions. Sorter 24S accesses each input position and sorts the candidates by character into locations within sort memory 24M. Identical candidates are sorted into adjacent locations in the sort memory forming the groups of like candidates. The search order of input register 22 by sorter 24S may be any suitable order; such as first to last, or highest CF to lowest CF, or 1st choices to 2nd choices to 3rd choices. Advance circuit 26 sequentially advances each group of candidates to JCF calculator 28C for calculation of the group JCF based on the joint probability equation. Output register 28R stores "J" group candidates and associated JCFs. The output of the selection agent back to he CPU may be the group candidate with the highest JCF, or the J highest JCFs. Alternatively the output may include all of the group candidates in order of JCF. For a complete pedigree of the original presented image, the output may include all of the information in input registers 22 including the address of the default image bit map. DATA FIELDS (Figure 2B and 2C) Each ICC candidate in the input registers may have related data arranged in data fields (see Figure 2B). Preferably the lead data field contains the ASCII code for the candidate (Can ASCII) followed by the CF of the candidate (Can CF). The address of the bit map of the presented image (Add DIm) in the CPU may be included for default image purposes, followed by a number or code identifying the processor (Pro Num) which generated the ASCII code. In addition the ICC candidates may have related text control codes (Text Cont) for preserving text printing requirements or document status information. The text codes may include word processing requirements such as bold or underlined text, and document wide considerations such as line spacing and font type. Each group candidate in output register 28R has corresponding related data such as the group ASCII code (Grp ASCII), the group joint confidence factor (Grp JCF), the default image address (Add DIm), and the text control codes (Text Cont). The processor number (Num Pro) is not applicable at the group level, and may be replaced by more useful information such as the number of candidates in the group (Num Can). The number of candidates is the number of CF terms in the CF equation, or the number of factors in JeP. ADVANCE CIRCUIT (Figure 3) Advance circuit 26 sequentially accesses the candidates in sort memory 24M and loads them into a suitable group detection device such as true/false comparator 32 (see Figure 3) for comparing ASCII codes. The comparator advances the candidates of each group to JCF calculator 28C for group multiplication and then to output register 28R. The comparator has a dominant side 32D and a non-dominant side 32N. The first candidate from the first group in the sort memory is loaded into the non-dominant side and immediately shifts to the dominant side and simultaneously advances to the JCF calculator. This first candidate is the lead candidate of the first group and through comparison advances each candidate (like ASCII code) in its group and detects the lead candidate (unlike ASCII code) in the next group. The next candidate in the first group is loaded into the non-dominant side for comparison with the lead candidate. The comparator determines whether the compare is true "T" or false "F". T/F controller 34C responds to the T/F output of the comparator by providing: a "T-Adv" signal when the comparison is true, a "F-Adv" signal when the comparison is false, and an "Advance" signal in either case. If the compare is true, the candidate in the non-dominant side advances to the JCF calculator (T-Adv), and the next candidate from the sort memory loads into the non-dominant side. For each true compare, the T/F controller provides a "Multiply" signal to the JCF calculator causing a multiplication between the advanced candidate and the product in the calculator. If the compare is false (F-Adv), the candidate in the non-dominant side shifts to the dominant side displacing the old lead candidate. Memory addresser 34A is incremented by each Advance signal to access the next candidate in the sort memory, which is loaded into the non-dominant side. For each false compare, the T/F controller provides a "Clear Group" signal to the JCF calculator in order to clear the calculator for the next group by advancing the JCF within the calculator to output register 28R. Clearly various changes may be made in the structure and embodiments shown herein without departing from the concept of the invention as defined by the claims. Further, features of the embodiments shown in the various Figures may be employed with the embodiments of the other Figures. Therefore, the scope of the invention is to be determined by the terminology of the following claims.

Claims: Character selection apparatus for receiving input character codes from a plurality of optical character recognition processors (14EX, 14NN) and for selecting the mathematically most probable code from among the plurality of input character codes based on a confidence factor associated with each input character code, the apparatus comprising: input means (22A-E) having a plurality of input positions for receiving the input character codes and the associated confidence factors; sort means (24S) for sorting the input character codes into groups of identical codes; sort memory means (24M) for storing the groups of sorted input character codes at memory locations within the sort memory means; joint confidence factor calculator means (28C) for determining a joint confidence factor for each group of identical codes based on the confidence factors for the input character codes in each group; advance means (26) for advancing each group of input character codes in the sort memory means (24M) to the joint confidence factor calculator means (28C) for determination of the group joint confidence factor; and output means (28R) for receiving the joint confidence factors from the joint confidence factor calculator means (28C) and for selecting a most probable symbol based on the relative joint confidence factors. The character selection apparatus of claim 1, wherein the advance means (26) further comprises: addressing means (34A) for sequentially accessing the sorted input character codes in the sort memory means (24M) for providing a sorted stream of input character codes; and group detector means (32) for monitoring the input character code stream to activate the joint confidence factor calculator means (28C) to execute the joint confidence factor calculation and to clear the joint confidence factor calculator means (28C) at the end of each group of identical input character codes. The character selection apparatus of claim 2, wherein: the input character code groups are stored at sequential locations in the sort memory means (24M); and the addressing means (34A) is adapted to sequentially access the stored input character code groups. The character selection apparatus of claim 3, wherein the group detector means (32) further comprises: a true/false comparator(32) for receiving the lead input character code in each group and for comparing the lead input character code to the subsequent input character code in the input character code stream to determine if the comparison is true or false. The character selection apparatus of claim 4, wherein the true/false comparator (32) comprises: a dominant portion (32D) for receiving the lead input character code in each group; and a non-dominant portion (32N) for receiving the subsequent input character codes. The character selection apparatus of claim 5, wherein the group detector means (32) further comprises: a true/false controller means (34C) responsive to the true/false comparator for advancing a subsequent input character code from the non-dominant portion (32N) of the true/false comparator (32) to the joint confidence factor calculator means (28C) when the comparison is true, advancing the lead input character code in the dominant portion (32D) of the true/false comparator (32) to the joint confidence factor calculator means (28C) when the comparison is false, and shifting the subsequent input character code from the non-dominant portion (32N) of the true/false comparator (32) to the dominant portion (32D) when the comparison is false. The character selection apparatus of claim 6, wherein the addressing means (34A) is adapted to be incremented in response to the advancing of the true/false controller means (34C) for accessing the next location in the sort memory means (24M) to provide the sorted stream of input character codes into the true/false comparator (32). The character selection apparatus of claim 1, wherein the input character codes are formatted by data fields, comprising: a character data field containing a binary character code of the input character code; and a confidence factor data field containing a binary value of the confidence factor for the character code contained in the character field. The character selection apparatus of claim 8, wherein the confidence factor data field contains probability of correctness data, and the output means contains joint probability data. The character selection apparatus of claim 8, wherein the confidence factor data field contains probability of error data, and the output means (28R) contains joint error product data. The character selection apparatus of claim 8, wherein the data field format of each input character code further comprises: a processor data field identifying which individual optical character recognition processor from the plurality of optical character recognition processors provided the input character code. The character selection apparatus of claim 8, wherein the data field format of each input character code further comprises: a text control field containing text control codes. The character selection apparatus of claim 8, wherein the data field format of each input character code further comprises: a default image field containing a processor address of an image for the binary character code in the character data field. The character selection apparatus of claim 13, wherein the input means (22A-E) has image bit map memories for receiving the character code images from the optical character recognition processors. The character selection apparatus of claim 14, wherein the output means (28R) has image bit map memories for receiving the character code images from the input means (22A-E). The character selection apparatus of claim 1, wherein the input means (22A-E) further comprises: N registers having input positions for receiving N input character codes corresponding to N character images processed by the optical character recognition processors; and C positions within each register for receiving C choices of input character codes from the N character images, where N and C are integers. The character selection apparatus of claim 1, wherein the output means (28R) further comprises: J output positions for storing J joint confidence factors from the joint confidence factor calculating means (28C), where J is an integer. The character selection apparatus of claim 1, wherein the joint confidence factor calculator means (28C) determines the joint confidence factor (JCF) of each group of n identical codes based on the relationship: JCF n = 1-(1- CF 1 ) (1- CF 2 ) ... (1- CF n ). The character selection apparatus of claim 18, wherein the confidence factor values are calibrated and are equal to the probability of correctness of the input character codes. The character selection apparatus of claim 19, wherein the joint confidence factor calculator means (28C) determines a joint probability (JP) of each group of n identical codes based on the relationship: JP n = 1-(1- P 1 ) (1- P 2 ) ... (1- P n ). The character selection apparatus of claim 20, wherein the joint confidence factor calculator means determines a joint probability (JP) of each group of n identical codes based on the relationship: JP n = 1-( e 1 ) ( e 2 ) ( e 3 ) ... ( e n ), where e is the probability of error and is determined by the relationship "P + e = 1". The character selection apparatus of claim 21, wherein the joint confidence factor calculator means (28C) determines a joint probability (JP) of each group of n identical codes based on the relationship: JP n =1- Jep n where Jep n is the product of the e terms. The character selection apparatus of claim 1, wherein: the sort means (24S) is adapted to sort the input character codes from the input means (22A-E) into the sort memory means (24M) in the order of a first position in the input means (22A-E) through to a last position in the input means (22A-E). The character selection apparatus of claim 1, wherein: the sort means (24S) is adapted to sort the input character codes from the input means (22A-E) into the sort memory means (24M) in the order of the input character code with the highest confidence factor through to the input character code with the lowest confidence factor. Apparatus for image recognition comprising: input means (10S) for receiving an image signal; recognition means (14EX, 14NN) for outputting a recognition signal corresponding to a predetermined recognised image and a measure of the likelihood of correspondence of the input image signal to the recognised image; decision means (16) for deciding, on the basis of said likelihood signal, whether said image has been recognised; and output means (28R) for outputting the recognised image; said recognition means (14EX, 14NN) comprising a plurality of recognition processors, each for processing the image signal, for operating in a functionally different way, and for generating, for a recognised image, an individual likelihood signal; said decision means (16) comprising means for forming groups of identical recognised images, and means for forming a joint likelihood signal for each group using the individual measures of the likelihood for each recognised image in the group; and said output means (28R) comprising means for selecting for output the recognised image based on the joint likelihood signals for the groups. Apparatus for image recognition comprising: input means (10S) for receiving an image signal; recognition means (14EX, 14NN) comprising a plurality of optical character recognition processors, each for processing the image signal, in different ways, and for generating character codes and respective confidence factors; and the character selection apparatus according to any one of claims 1 to 24. The apparatus of claim 26 including memory means (12M) for storing said image signal, and detecting means for detecting unrecognised characters in said image signal and for indicating stored image data in said memory means which corresponds to said unrecognised characters. The apparatus of claim 27 wherein detecting means is adapted to output the address of said image data for said unrecognised character in said memory means (12M). The apparatus of claim 26 including means for recognising and retaining character attributes of characters in said input image signal, and for generating format identification data for output with said selected most probable symbol. A character selection method for receiving input character codes for a plurality of optical character recognition processors (14EX, 14NN) and for selecting the mathematically most probable code from among the plurality of input character codes based on a confidence factor associated with each input character code, the method comprising the steps of: receiving the input character codes and the associated confidence factors from input means (22A-E) having a plurality of input positions; sorting the input character codes into groups of identical codes; storing the groups sorted input character codes at memory locations; determining a joint confidence factor for each group of identical codes based on the confidence factors for the input character codes in each group; and selecting the most probable symbol based on the relative joint confidence factors. The method of claim 30 wherein the sorted input character codes are sequentially accessed to provide a sorted stream of input character codes for the determination of the group joint confidence factor, and the sorted input character code stream is monitored to activate the calculation of the joint confidence factors and to clear the calculation of the joint confidence factor at the end of each group of identical input character codes. The method of claim 31 wherein the input character code groups are stored at sequential memory locations and the stored input character code groups are sequentially accessed. The method of claim 32 wherein the lead input character code in each group is received and compared with the lead input character code of subsequent input character codes in the input character code stream to determine if the comparison is true or false. The method of claim 33 wherein the comparison is carried out by receiving the lead input character code in a dominant portion of a comparator and receiving the subsequent input character codes in a non-dominant portion of the comparator. The method of claim 34 wherein a subsequent input character code is advanced from the non-dominant side of the comparator to be used in the joint confidence factor calculation when the comparison is true, the lead input character code in the dominant portion of the comparator is advanced to the joint confidence factor calculation when the comparison is false, and the subsequent input character code is shifted from the non-dominant portion of the comparator to the dominant portion when the comparison is false. A method of claim 35 wherein the memory address is incremented to provide the sorted stream of input character codes into the comparator. The method of claim 30 wherein the input character codes are formatted by data fields, comprising a character data field containing a binary character code of the input character code, and a confidence factor data field containing a binary value of the confidence factor for the character code contained in the character field. A method of claim 37 wherein the confidence factor data field contains probability of correctness data and a joint probability data is output. The method of claim 37 wherein the confidence factor data field contains probability of error data, and joint error product data is output. The method of claim 37 wherein the data field format of each input character code further comprises a process data field identifying which individual optical character recognition processor from the plurality of optical character recognition processors provided the input character code. The method of claim 37 wherein the data field format of each input character code further comprises a text control field containing text control codes. The method of claim 37 wherein the data field format of each input character code further comprises a default image field containing a processor address of an image for the binary character code in the character data field. The method of claim 32 including the step of receiving the character code images from the optical character recognition processors into image bit map memories. The method of claim 43 wherein N input character codes are input into N registers for processing by the optical character recognition processors, and couple C choices of input character codes from the N character codes are stored in C positions within each register, where N and C are integers. The method of claim 30 wherein J joint confidence factors are stored in J output positions, where J is an integer. The method of claim 30 wherein the joint confidence factor (JCF) of each group of n identical codes is calculated based on the relationship: JCF n = 1-(1- CF 1 ) (1- CF 2 ) ... (1- CF n ). The method of claim 46 wherein the confidence factor values are calibrated and are equal to the probability of correctness of the input character codes. The method of claim 47 wherein a joint probability (JP) of each group of n identical codes is calculated based on the relationship: JP n = 1-(1- P 1 ) (1- P 2 ) ... (1- P n ). The method of claim 48 wherein a joint probability (JP) of each group of n identical codes is calculated based on the relationship: JP n = 1-( e 1 ) ( e 2 ) ( e 3 )... ( e n ), where e is the probability of error and is determined by the relationship "P + e = 1". The method of claim 49 wherein a joint probability (JP) of each group of n identical codes is calculated based on the relationship: JP n = 1- Jep n where Jep n is the product of the e terms. The method of claim 30 wherein the input character codes are sorted in the order of a first position in an input register through a last position in an input register. The method of claim 30 wherein the input character codes are sorted in the order of the input character code with the highest confidence factor through to the input character code with the lowest confidence factor. A method of image recognition comprising the steps of: receiving an image signal; outputting a recognition signal corresponding to a predetermined recognised image and a measure of the likelihood of correspondence of the input image signal to the recognised image; deciding, on the basis of said likelihood signal, whether said image has been recognised; and outputting the recognised image; wherein the recognition is carried out by a plurality of recognition processors each processing the image signal, operating in a functionally different way, and generating, for a recognised image, an individual likelihood signal; groups of identical recognised images are formed, and a joint likelihood signal for each group is formed using the individual measures of the likelihood for each recognised image in the group; and a recognised image is selected for output based on the joint likelihood signals for the groups. A method of image recognition comprising the steps of: receiving an image signal; processing the image signal in a priority of individual character recognition processors in different ways to generate character codes and respective confidence factors; and the character selection method according to any one of claims 30 to 53. A method of claim 54 including the steps of storing said image signal in memory means, detecting unrecognised characters in said image signal, and indicating stored image data in said memory means which corresponds to said unrecognised characters. A method of claim 55 wherein the stored image data for unrecognised characters is indicated by outputting the address of the image data in said memory means. A method of claim 54 including the steps of recognising character attributes of characters in said input image signal, and generating format identification data for output with said selected most probable symbol.

Inventors: GARLAND HARRY T, MELEN ROGER D, GARLAND, HARRY T., MELEN, ROGER D., Garland, Harry T., c/o Canon Kabushiki Kaisha, Melen, Roger D., c/o Canon Kabushiki Kaisha
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1992
Main classification: G06K9
IPC codes: G06K9