Method and system using meta-classes and polynomial discriminant functions for handwriting recognition

Claim 2

In a computer, a method of identifying text in a handwriting input, the method comprising the following steps: extracting a plurality of features from the handwriting input; generating a meta-class signal by distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another; distributing the plurality of features to a plurality of character classifiers for distinguishing between the meta-class characters; generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU3## wherein x j represents the plurality of features; wherein i, j, m and n are integers; wherein y represents one of the character classifier output signals; wherein w i represents a coefficient; and wherein g ji represents an exponent; and identifying the handwritten text as a function of the meta-class signal and the plurality of character classifier output signals.

Claim 3

The method of claim 1, wherein at least one of the polynomial discriminant functions represents a second order polynomial.

Claim 4

The method of claim 2, wherein the at least one polynomial discriminant function has the form ##EQU4## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ij represents a second-order coefficient.

Claim 5

The method of claim 1, wherein the step of identifying includes the following sub-step: distributing the plurality of features to a plurality of discriminant functions, each of the plurality of discriminant functions designating a different character.

Claim 6

The method of claim 1, wherein the step of extracting includes the following sub-steps: receiving a sequence of coordinate-pairs that represent the handwriting input; low-pass filtering the sequence of coordinate-pairs; removing duplicate coordinate-pairs from the sequence of coordinate-pairs; partitioning the sequence of coordinate-pairs into a sequence of data frames; and extracting the plurality of features from the sequence of data frames.

Claim 7

The method of claim 1, wherein the step of identifying includes the following sub-step: comparing a value based on one of the character classifier output signals to a lexicon to generate a signal representing the text.

Claim 8

In a computer, a method of identifying text in a handwriting input, the method comprising the following steps: extracting a plurality of features from a data frame that represent the handwriting input; generating a meta-class signal by distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another; distributing the plurality of features to a plurality of meta-class character classifiers trained using localized features to distinguish between the meta-class characters; generating a plurality of meta-class character classifier output signals based on a plurality of polynomial discriminant functions classifying the plurality of features according to a discriminant function based on a polynomial expansion having a form ##EQU5## wherein x j represents the plurality of features; wherein I, j, m and n are integers; wherein y represents a discriminant function output signal; wherein w i represents a coefficient; and wherein g ji represents an exponent; accumulating the meta-class signal over a sequence of data frames to generate a meta-class confidence value; accumulating the meta-class character classifier output signals over the sequence of data frames to generate a plurality of meta-class character confidence values; and identifying the text as a function of the meta-class and meta-class character confidence values.

Claim 9

In a computer, a method of identifying text in a handwriting input, the method comprising the following steps: extracting a plurality of features from a data frame that represent the handwriting input; generating a meta-class signal by distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another; distributing the plurality of features to a plurality of meta-class character classifiers trained using localized features to distinguish between the meta-class characters; generating a plurality of meta-class character classifier output signals based on a plurality of polynomial discriminant functions classifying the plurality of features according to a discriminant function based on a polynomial expansion having a form ##EQU5## wherein x j represents the plurality of features; wherein I, j, m and n are integers; wherein y represents a discriminant function output signal; wherein w i represents a coefficient; and wherein g ji represents an exponent; accumulating the meta-class signal over a sequence of data frames to generate a meta-class confidence value; accumulating the meta-class character classifier output signals over the sequence of data frames to generate a plurality of meta-class character confidence values; and identifying the text as a function of the meta-class and meta-class character confidence values.

Claim 10

The method of claim 7, wherein at least one of the polynomial discriminant functions represents a second order polynomial.

Claim 11

The method of claim 8, wherein the at least one polynomial discriminant function has the form ##EQU6## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ji represents a second-order coefficient.

Claim 12

The method of claim 7, further comprising the following steps: receiving a sequence of coordinate-pairs that represent the handwriting input; low-pass filtering the sequence of coordinate-pairs; removing duplicate coordinate-pairs from the sequence of coordinate-pairs; and partitioning the sequence of coordinate-pairs into the sequence of data frames.

Claim 13

The method of claim 7, wherein the step of identifying includes the following sub-step: comparing the meta-class and meta-class character confidence values to a lexicon to generate a signal representing the text.

Claim 14

In a computer, a method of identifying text in a handwriting input, the method comprising the following steps: receiving a sequence of coordinate-pairs that represent the handwriting input; low-pass filtering the sequence of coordinate-pairs; removing duplicate coordinate-pairs from the sequence of coordinate-pairs; partitioning the sequence of coordinate-pairs into a sequence of data frames; extracting a plurality of features from the sequence of data frames; distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another, generating a meta-class classifier output signal according to a meta-class polynomial discriminant function; distributing the plurality of features to a plurality of meta-class character classifiers for distinguishing between the meta-class characters; generating a plurality of meta-class character classifier output signals based on a plurality of meta-character polynomial discriminant functions, each of the meta-class character polynomial discriminant functions having a form ##EQU7## wherein x j represents the plurality of features; wherein i, j, m and n are integers; wherein y l represents one of the meta-class character classifier output signals; wherein w li represents a meta-class coefficient; and wherein g lji represents a meta-class exponent; accumulating the meta-class classifier output signal over the sequence of data frames to generate a meta-class confidence value; accumulating the meta-class character classifier output signals over the sequence of data frames to generate a plurality of meta-class character confidence values; and identifying the text as a function of the meta-class and meta-class character confidence values.

Claim 15

In a computer, a method of identifying text in a handwriting input, the method comprising the following steps: receiving a sequence of coordinate-pairs that represent the handwriting input; low-pass filtering the sequence of coordinate-pairs; removing duplicate coordinate-pairs from the sequence of coordinate-pairs; partitioning the sequence of coordinate-pairs into a sequence of data frames; extracting a plurality of features from the sequence of data frames; distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another, generating a meta-class classifier output signal according to a meta-class polynomial discriminant function; distributing the plurality of features to a plurality of meta-class character classifiers for distinguishing between the meta-class characters; generating a plurality of meta-class character classifier output signals based on a plurality of meta-character polynomial discriminant functions, each of the meta-class character polynomial discriminant functions having a form ##EQU7## wherein x j represents the plurality of features; wherein i, j, m and n are integers; wherein y l represents one of the meta-class character classifier output signals; wherein w li represents a meta-class coefficient; and wherein g lji represents a meta-class exponent; accumulating the meta-class classifier output signal over the sequence of data frames to generate a meta-class confidence value; accumulating the meta-class character classifier output signals over the sequence of data frames to generate a plurality of meta-class character confidence values; and identifying the text as a function of the meta-class and meta-class character confidence values.

Claim 16

The method of claim 12, wherein at least one of the meta-class character polynomial discriminant functions represents a second order polynomial.

Claim 17

The method of claim 13, wherein the at least one meta-class character polynomial discriminant function has the form ##EQU8## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ij represents a second-order coefficient.

Claim 18

The method of claim 12, wherein the meta-class polynomial discriminant function is based on a polynomial expansion having the form ##EQU9## wherein y 2 represents a meat-class classifier output signal; wherein w 2i represents a class coefficient; and wherein g 2ji represents a class exponent.

Claim 19

The method of claim 15, wherein the meta-class polynomial discriminant function represents a second-order polynomial.

Claim 20

The method of claim 16, wherein the meta-class polynomial discriminant function has the form ##EQU10## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ij represents a second-order coefficient.

Claim 21

The method of claim 12, wherein the step of identifying includes the following sub-step: comparing at least one of the plurality of confidence values to a lexicon to generate a signal representing the text.

Claim 22

An article of manufacture, which comprises: a computer-readable memory for directing a computer to recognize a handwriting input, the computer-readable memory having a structure defined by storing a computer program in the computer-readable memory; wherein the computer program includes a method for identifying text in the handwriting input, the method comprising the steps of extracting a plurality of features from the handwriting input, generating a meta-class signal by distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another, distributing the plurality of features to a plurality of character classifiers for distinguishing between the meta-class characters, generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU11## wherein x j represents the plurality of features, wherein i, j, m and n are integers, wherein y represents one of the character classifier output signals, wherein w i represents a coefficient, and wherein g ji represents an exponent, and identifying the handwritten text as a function of the meta-class signal and the plurality of character classifier output signals.

Claim 23

An article of manufacture, which comprises: a computer-readable memory for directing a computer to recognize a handwriting input, the computer-readable memory having a structure defined by storing a computer program in the computer-readable memory; wherein the computer program includes a method for identifying text in the handwriting input, the method comprising the steps of extracting a plurality of features from the handwriting input, generating a meta-class signal by distributing the plurality of features to a meta-class classifier for identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another, distributing the plurality of features to a plurality of character classifiers for distinguishing between the meta-class characters, generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU11## wherein x j represents the plurality of features, wherein i, j, m and n are integers, wherein y represents one of the character classifier output signals, wherein w i represents a coefficient, and wherein g ji represents an exponent, and identifying the handwritten text as a function of the meta-class signal and the plurality of character classifier output signals.

Claim 24

The article of manufacture of claim 19, wherein at least one of the polynomial discriminant functions represents a second order polynomial.

Claim 25

The article of manufacture of claim 20, wherein the at least one polynomial discriminant function has a form ##EQU12## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ij represents a second-order coefficient.

Claim 26

The article of manufacture of claim 19, wherein the step of identifying includes the following sub-step: distributing the plurality of features to a plurality of discriminant functions, each of the plurality of discriminant functions designating a different character.

Claim 27

The article of manufacture of claim 19, wherein the step of extracting includes the following sub-steps: receiving a sequence of coordinate-pairs that represent the handwriting input; low-pass filtering the sequence of coordinate-pairs; removing duplicate coordinate-pairs from the sequence of coordinate-pairs; partitioning the sequence coordinate-pairs into a sequence of data frames; and extracting the plurality of features from the sequence of data frames.

Claim 28

The article of manufacture of claim 19, wherein the step of identifying includes the following sub-step: comparing a value based on the meta-class signal and the plurality of character classifier output signals to a lexicon to generate a signal representing the text.

Claim 29

A handwriting recognition system for identifying text in a handwriting input, which comprises: a feature extractor for extracting a plurality of features from the handwriting input; a meta-class classifier generating a meta-class signal in response to the plurality of features, the meta-class classifier identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another: a plurality of character classifiers for distinguishing between the meta-class characters, the character classifiers generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU13## wherein x j represents the plurality of features, i, j, m and n are integers, y represents one of the character classifier output signals, w i represents a coefficient, and g ji represents an exponent; and a selector identifying the handwritten text as a function of the meta-class signal and the plurality of character classifier output signals.

Claim 30

A handwriting recognition system for identifying text in a handwriting input, which comprises: a feature extractor for extracting a plurality of features from the handwriting input; a meta-class classifier generating a meta-class signal in response to the plurality of features, the meta-class classifier identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another: a plurality of character classifiers for distinguishing between the meta-class characters, the character classifiers generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU13## wherein x j represents the plurality of features, i, j, m and n are integers, y represents one of the character classifier output signals, w i represents a coefficient, and g ji represents an exponent; and a selector identifying the handwritten text as a function of the meta-class signal and the plurality of character classifier output signals.

Claim 31

The handwriting recognition system of claim 25, wherein at least one of the polynomial discriminant functions represents a second order polynomial.

Claim 32

The handwriting recognition system of claim 25, the at least one polynomial discriminant function has a form ##EQU14## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ij represents a second-order coefficient.

Claim 33

The handwriting recognition system of claim 25, further comprising: a lexical post processor for comparing a value based on the meta-class signal and the plurality of character classifier output signals to a lexicon to generate a signal representing the text.

Claim 34

A handwriting recognition system for identifying text in a handwriting input, which comprises: a pre-processor, responsive to a sequence of coordinate-pairs representing the handwriting input, for low-pass filtering the sequence of coordinate-pairs to generate a filtered sequence; a frame extractor for partitioning the filtered sequence into a sequence of data frames; a feature extractor for extracting a plurality of features from the sequence of data frames; a meta-class classifier generating a meta-class signal in response to the plurality of features, the meta-class classifier identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another: a plurality of character classifiers for distinguishing between the meta-class characters, the character classifiers generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU15## wherein x j represents the plurality of features, i, j, m and n are integers, y represents one of the character classifier output signals, w i represents a coefficient, and g ji represents an exponent: a meta-class accumulator for accumulating the meta-class signal over the sequence of data frames to generate a meta-class confidence value; a plurality of meta-character accumulators for accumulating the meta-class character signals over the sequence of data frames to generate a plurality of meta-class character confidence values; and a selector identifying the text as a function of the meta-class and meta-class character confidence values.

Claim 35

A handwriting recognition system for identifying text in a handwriting input, which comprises: a pre-processor, responsive to a sequence of coordinate-pairs representing the handwriting input, for low-pass filtering the sequence of coordinate-pairs to generate a filtered sequence; a frame extractor for partitioning the filtered sequence into a sequence of data frames; a feature extractor for extracting a plurality of features from the sequence of data frames; a meta-class classifier generating a meta-class signal in response to the plurality of features, the meta-class classifier identifying a meta-class comprising a plurality of meta-class characters that are not easily discernible from one another: a plurality of character classifiers for distinguishing between the meta-class characters, the character classifiers generating a plurality of character classifier output signals based on a plurality of polynomial discriminant functions, each of the polynomial discriminant functions having a form ##EQU15## wherein x j represents the plurality of features, i, j, m and n are integers, y represents one of the character classifier output signals, w i represents a coefficient, and g ji represents an exponent: a meta-class accumulator for accumulating the meta-class signal over the sequence of data frames to generate a meta-class confidence value; a plurality of meta-character accumulators for accumulating the meta-class character signals over the sequence of data frames to generate a plurality of meta-class character confidence values; and a selector identifying the text as a function of the meta-class and meta-class character confidence values.

Claim 36

The handwriting recognition system of claim 29, wherein at least one of polynomial discriminant functions represents a second order polynomial.

Claim 37

The handwriting recognition system of claim 30, wherein the at least one polynomial discriminant function has a form ##EQU16## wherein a 0 represents a zero-order coefficient, b i represents a first-order coefficient, and c ij represents a second-order coefficient.

Claim 38

The handwriting recognition system of claim 29, further comprising: a lexical post processor for comparing the meta-class and meta-class character confidence values to a lexicon to generate a signal representing the text.

Claim 39

The handwriting recognition system of claim 29, wherein the pre-processor removes duplicate coordinate-pairs.