Method and system for optical character recognition

Claim 2

Method for optical character recognition comprising the steps of: A. optically scanning a character-to-be-recognized to identify it as one of a plurality of predetermined vocabulary characters, detecting the optical density of n regions of each scan, said regions being arranged to form a multiple element set of grid cells arranged in a grid of m rows and n columns, and generating a binary signal representative of the optical density of each of said cells, said binary signal being 1 when the optical density of a region exceeds a predetermined threshold and 0 otherwise, so that each cell of said set has the binary value associated with the correspondingly positioned region of said character-to-be-recognized, B. generating a series of multiple bit words, each word representing a rectangular arranged subset of said grid cells, the subset including p rows and q columns, where p is an integer less than m and q is an integer less than n, and wherein each word in the series represents a differing subset, C. determining the presence or absence of r features in each word of said series, where r is an integer less than the quantity 2mn, and each feature is defined as being present in a word when said word includes a predetermined distribution of binary values, said feature being defined as absent otherwise, D. generating and storing a multiple bit current vector signal for said character-to-be-recognized, said current vector signal having a binary 1 for each feature detected as present and a binary 0 for each feature detected as absent in each of said words, wherein each bit position in said current vector signal is associated with one of said words, E. generating and storing a plurality of multiple bit mask vector signals, each representing a different one of said predetermined plurality of vocabulary characters, wherein each bit position in each of said mask vector signals is associated with the same one of said words as the corresponding bit position in said current vector signal, F. comparing said current vector signal with said plurality of stored mask vector signals on a bit-by-bit basis, and G. identifying the mask vector signal which has highest correlation with current vector signal as the character-to-berecognized.

Claim 3

The method of claim 1 where only two features are determined to be present or absent in each of said words.

Claim 4

A method in accordance with claim 2 wherein m 15, n 18, p q 3 and the first feature is present in a word of said series if there are at least two adjacent binary 1 cells in any row of said subset, or at least two adjacent binary 1 cells in the first column of said subset, and said first feature is absent otherwise; and wherein further: the second feature is present in a word of said series if there is a binary 0 cell flanked by two adjacent binary 0 cells in any corner of said subset, and said second feature is absent otherwise.

Claim 5

A method in accordance with claim 1 wherein one feature is characterized by a predetermined distribution of binary one values in each of said series of multiple bit words and a second feature is characterized by a predetermined distribution of binary zero values in one of said multiple bit words, not the complement of said first feature.

Claim 6

A method in accordance with claim 4 wherein m 15, n 18, p q 3 and the first feature is present in a word of said series if there are at least two adjacent binary 1 cells in any row of said subset, or at least two adjacent binary 1 cells in the first column of said subset, and said first feature is absent otherwise; and wherein further: the second feature is present in a word of said series if there is a binary 0 cell flanked by two adjacent binary 0 cells in any corner of said subset and said second feature is absent otherwise.

Claim 7

A method in accordance with claim 1 wherein said character-to-be-recognized is scanned along a series of n parallel columns of scan, wherein each column extends beyond the limits of said character in a first direction and said series of columns extends beyond said character in a second direction perpendicular to said first direction, and wherein the determination of the presence or absence of said features depends upon one set of determining rules for subsets including only cell locations within the limits of said character and upon a different set of rules for subsets which include cells outside of said limits.

Claim 8

A method in accordance with claim 1 wherein the presence or absence of an additional set of s special features is determined for each word of said series and said multiple bit current vector signal has a binary 1 or a binary 0 in each of s predetermined bit locations to indicate the presence or absence of said special features and wherein the correlations of said mask vector signals with a current vector signal includes said predetermined bit locations for only s ones of said plurality of characters-to-be-recognized, s being a small fraction of said plurality.

Claim 9

A method in accordance with claim 1 wherein each of said mask vector signals is generated by a process of: scanning one of a plurality of a predetermined ideal reference characters and generating a series of multiple bit words in accordance with steps A and B of said claim 1, and making a series of determinations of the presence or absence of r features and generating therefrom a series of multiple bit preliminary mask vector signals, each having a binary 1 for each feature detected as present and a binary 0 for each feature detected as absent, the first preliminary mask vector signal of said series being for the set of words in said series which represents a grid of rows and columns centered on and co-extensive with said character and the remaining preliminary mask vector signals being for one or more additional sets of words in said series representing a displacement of said grid in the direction either of said rows or said columns by an integral number of cell spaces, and generating the final mask vector signal for each character as a word having binary 1 values only in those bit positions for which a binary 1 value existed in each of the preliminary vector signals.

Claim 10

An optical character recognition system comprising: A. means for optically scanning a character-to-be-recognized to identify it as one of a plurality of predetermined vocabulary characters including: i. means for detecting the optical density of n regions of each scan, said regions being arranged to form a multiple cell set arranged in a grid of m rows and n columns, and ii. means for generating a binary signal representative of the optical density of each of said cells, said binary signal being 1 when the optical density of a region exceeds a predetermined threshold and 0 otherwise, so that each cell of said set has the binary value associated with the correspondingly positioned region of said character-to-be-recognized, B. means for generating a series of multiple bit words, each word representing a rectangular arranged subset of said grid cells, the subset including p rows and q columns, where p is an integer less than m and q is an integer less than n, and wherein each word in the series represents a different subset, C. means for determining the presence or absence of r features in each word of said series, where r is an integer less than the quantity 2mn, and each feature is defined as being present in a word when said word includes a predetermined distribution of binary values, said feature defined as being absent otherwise, D. means for generating and storing a multiple bit current vector signal for said character-to-be-recognized, said current vector signal having a binary 1 for each feature detected as present and a binary 0 for each feature detected as absent in each of said words, wherein each bit position in said current vector signal is associated with one of said words, E. means for generating and storing a plurality of multiple bit mask vector signals, each representing a different one of said predetermined plurality of vocabulary characters, wherein each bit position in each of said mask vector signals is associated with the same one of said words as the corresponding bit position in said current vector signal, F. means for comparing said current vector signal with said plurality of stored mask vector signals on a bit-by-bit basis and G. means for identifying the mask vector signal which has highest correlation with current vector signal as the character-to-be-recognized.

Claim 11

An optical character recognition system comprising: A. means for optically scanning a character-to-be-recognized to identify it as one of a plurality of predetermined vocabulary characters including: i. means for detecting the optical density of n regions of each scan, said regions being arranged to form a multiple cell set arranged in a grid of m rows and n columns, and ii. means for generating a binary signal representative of the optical density of each of said cells, said binary signal being 1 when the optical density of a region exceeds a predetermined threshold and 0 otherwise, so that each cell of said set has the binary value associated with the correspondingly positioned region of said character-to-be-recognized, B. means for generating a series of multiple bit words, each word representing a rectangular arranged subset of said grid cells, the subset including p rows and q columns, where p is an integer less than m and q is an integer less than n, and wherein each word in the series represents a different subset, C. means for determining the presence or absence of r features in each word of said series, where r is an integer less than the quantity 2mn, and each feature is defined as being present in a word when said word includes a predetermined distribution of binary values, said feature defined as being absent otherwise, D. means for generating and storing a multiple bit current vector signal for said character-to-be-recognized, said current vector signal having a binary 1 for each feature detected as present and a binary 0 for each feature detected as absent in each of said words, wherein each bit position in said current vector signal is associated with one of said words, E. means for generating and storing a plurality of multiple bit mask vector signals, each representing a different one of said predetermined plurality of vocabulary characters, wherein each bit position in each of said mask vector signals is associated with the same one of said words as the corresponding bit position in said current vector signal, F. means for comparing said current vector signal with said plurality of stored mask vector signals on a bit-by-bit basis and G. means for identifying the mask vector signal which has highest correlation with current vector signal as the character-to-be-recognized.

Claim 12

The system of claim 9 where only two features are determined to be present or absent in each of said words.

Claim 13

A system in accordance with claim 10 wherein m 15, n 18, p q 3, and the first feature is present in a word of said series if there are at least two adjacent binary 1 cells in any row of said subset, or at least two adjacent binary 1 cells in the first column of said subset, and said first feature is absent otherwise; and wherein further: the second feature is present in a word of said series if there is a binary 0 cell flanked by two adjacent binary 0 cellS in any corner of said subset, and said second feature is absent otherwise.

Claim 14

A system in accordance with claim 9 wherein one feature is characterized by a predetermined distribution of binary one values in each of said series of multiple bit words and a second feature is characterized by a predetermined distribution of binary zero values in one of said multiple bit words, not the complement of said first feature.

Claim 15

A system in accordance with claim 12 wherein m 15, n 18, p q 3, and the first feature is present in a word of said series if there are at least two adjacent binary 1 cells in any row of said subset, or at least two adjacent binary 1 cells in the first column of said subset, and said first feature is absent otherwise; and wherein further: the second feature is present in a word of said series if there is a binary 0 cell flanked by two adjacent binary 0 cells in any corner of said subset, and said second feature is absent otherwise.

Claim 16

A system in accordance with claim 9 wherein said character-to-be-recognized is scanned along a series of n parallel columns of scan, wherein each column extends beyond the limits of said character in a first direction and said series of columns extends beyond said character in a second direction perpendicular to said first direction, and wherein the determination of the presence or absence of said features depends upon one set of determining rules for subsets including only cell locations within the limits of said character and upon a different set of rules for subsets which include cells outside of said limits.

Claim 17

A system in accordance with claim 9 wherein the presence or absence of an additional set of s special features is determined for each word of said series and said multiple bit current vector signal has a binary 1 or a binary 0 in each of s predetermined bit locations to indicate the presence or absence of said special features and wherein the correlations of said mask vector signals with a current vector signal includes said predetermined bit locations for only s ones of said plurality of characters-to-be-recognized, s being a small fraction of said plurality.

Claim 18

A system in accordance with claim 9 further comprising a means for generating said mask vector signals, said mask vector signal generating means including: A. means for optically scanning each of a plurality of predetermined ideal reference character, each ideal reference character corresponding to one of said predetermined vocabulary characters, said ideal character scanning means comprising: i. means for detecting the optical density of n regions of each scan for each ideal reference character, said regions being arranged to form a multiple cell set arranged in a grid of m rows and n columns, and ii. means for generating a binary signal for each scanned ideal reference character representative of the optical density of each of said cells, said binary signal being 1 when the optical density of a region exceeds a predetermined threshold and 0 otherwise, so that each cell of said set has the binary value associated with the correspondingly positioned region of said scanned ideal reference character, B. means for generating a series of multiple bit words for each scanned ideal reference character, each word representing a rectangular arranged subset of said grid cells, the subset including p rows and q columns, where p is an integer less than m and q is an integer less than n, and wherein each word in the series represents a different subset, C. means for making a series of determinations of the presence or absence of r features in each of said words for each of said scanned ideal reference character, D. means for generating a series of multiple bit preliminary mask vector signals associated with said words for each scanned ideal reference character, each pReliminary mask vector signal having a binary 1 for each feature detected by said determination means as present and a binary 0 for each feature detected by said determination means as absent, said first preliminary mask vector signal being for the set of words in said series which represents a grid of rows and columns centered on and co-extensive with said scanned character and the remaining preliminary mask vector signals being for one or more additional sets of words in said series representing a displacement of said grid in the direction either of said rows or said columns by an integral number of cell spaces, and E. means for generating the final mask vector signal for each scanned ideal reference character as a word having binary 1 values only in those bit positions for which a binary 1 value existed in each of said associated preliminary mask vector signals.