Handwriting signal processing front-end for handwriting recognizers

A front-end processing method for a handwriting recognition system, said method for processing strokes of handwriting test samples comprising a time series of (x,y) coordinates, wherein said handwriting recognition system has been trained from handwriting training samples including a plurality of feature-specific vectors and codebooks generated therefrom, wherein each of said codebooks is generated for each of said feature-specific vectors, said codebooks comprising mean values and symbols for a plurality of clusters, said method comprising the steps of:

• segmenting said strokes based on interrelationships of said (x,y) coordinates into an ordered set of test stroke segments that are non-uniform in length for each of said handwriting test samples;
• extracting a first plurality of feature values from each of said test stroke segments, wherein each of said feature values extracted therefrom forms entries of a word-independent test feature vector;
• creating a series of test feature-specific vectors by grouping said entries corresponding to one of said feature values from contiguous groups of said word-independent test feature vectors;
• performing multiple vector quantization by vector quantizing each of said test feature-specific vectors to compare each of said test feature-specific vectors to said mean values in said codebooks, wherein said vector quantizing includes assigning to each of said test feature-specific vectors, one of said symbols of one of said mean values to which each of said test feature-specific vectors is closest in distance to form a series of output symbols;
• and
• using said output symbols to represent said handwriting test samples for recognition of said handwriting test samples.

A front-end processing method for training a handwriting recognizer from a training set of sampled handwriting data comprising a time series of sample points in the form of (x,y) coordinates, said method including the steps of:

• performing non-uniform segmentation on said series of sample points to partition said sample points into a first segment;
• extracting a first feature value and a second feature value from said first segment;
• creating a first word-independent vector from said first and second feature values;
• creating a first feature-specific vector from said first feature value in said first word-independent vector;
• creating a second feature-specific vector from said second feature value in said first word-independent vector;
• performing multiple vector quantization on said first word-independent vector by vector quantizing said first feature-specific vector to form a first cluster, and by vector quantizing said second feature-specific vector to form a second cluster, wherein each of said first and second clusters include a respective mean value and a distribution about said mean value;
• and
• storing said first cluster in a first codebook and storing said second cluster in a second codebook.

A front-end processing method for training a handwriting recognizer from a training set of sampled handwriting data comprising a series of sample points in the form of (x,y) coordinates, said method including the steps of:

• performing non-uniform segmentation on said series of sample points to partition said sample points into a first and a second segment;
• extracting a first and a second feature value from said first segment, and extracting said first and second feature values from said second segment;
• creating a first word-independent vector from said first and second feature values extracted from said first segment wherein said first and second feature values form entries in said first vector;
• creating a second word-independent vector from said first and second feature values extracted from said second segment wherein said first and second feature values form entries in said second vector;
• combining said first word-independent vector and said word-independent second vector to create an input frame;
• creating a first feature-specific vector by grouping said entries from said first word-independent vector and said second word-independent vector in said input frame corresponding to said first feature value;
• creating a second feature-specific vector by grouping said entries from said first word-independent vector and said second word-independent vector corresponding to said second feature value;
• performing multiple vector quantization on said input frame by vector quantizing said first feature-specific vector to statistically model said first feature-specific vector to create a first codebook, and by vector quantizing said second feature-specific vector to statistically model said second feature-specific vector to form a second codebook;
• and
• training said handwriting recognizer using said first and second codebooks.

A front-end processing method for a handwriting recognizer that has been trained using training data represented as a plurality of codebooks to recognize sampled handwriting data in the form of a series of sample points as known strings of characters, each of said codebooks including a plurality of clusters identified by symbols, said method including the steps of:

• performing non-uniform segmentation on said series of sample points to partition said sample points into a first and a second segment;
• extracting a first and a second feature value from said first segment, and extracting said first and second feature values from said second segment;
• creating a first word-independent vector from said first and second feature values extracted from said first segment wherein said first and second feature values form entries in said first word-independent vector;
• creating a second word-independent vector from said first and second feature values extracted from said second segment wherein said first and second feature values form entries in said second word-independent vector;
• combining said first word-independent vector and said second word-independent vector to create an input frame;
• creating a first feature-specific vector by grouping said entries from said first word-independent vector and said second word-independent vector in said input frame corresponding to said first feature value;
• creating a second feature-specific vector by grouping said entries from said first word-independent vector and said second word-independent vector corresponding to said second feature value;
• performing multiple vector quantization on said input frame by vector quantizating said first feature-specific vector to assign to said first feature-specific vector a first symbol of one of said clusters in one of said codebooks for which said first feature-specific vector is the closest, and by vector quantizating said second feature-specific vector to assign to said second feature-specific vector a second symbol of one of said clusters in one of said codebooks for which said second feature-specific vector is the closest;
• and
• sending said first and second symbols to said handwriting recognizer for recognition as said known strings of characters.