System and method for automatic handwriting recognition with a writer-independent chirographic label alphabet
First Claim
1. A method for operating a handwriting recognition system having a programmed digital data processor, comprising the data processor executed steps of:
- generating a sequence of feature vectors in response to a two-dimensional trace associated with the input handwriting;
projecting the sequence of feature vectors onto a higher dimensional feature space that represents sub-character sections of the input handwriting;
performing a fast match of the sequence of feature vectors by generating a list of probable candidate characters using single-state, hidden Markov models (HMMs) having output probability distributions obtained from writer-independent higher dimensional feature space prototype distributions; and
performing a detailed match on the list of probable candidate characters using multi-state, writer independent HMMs to identify a most probable character sequence that the input handwriting represents;
whereinat least one of the steps of performing a fast match and performing a detailed match operate in accordance with a supervision process.
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Abstract
An automatic handwriting recognition system wherein each written (chirographic) manifestation of each character is represented by a statistical model (called a hidden Markov model). The system implements a method which entails sampling a pool of independent writers and deriving a hidden Markov model for each particular character (allograph) which is independent of a particular writer. The HMMs are used to derive a chirographic label alphabet which is independent of each writer. This is accomplished during what is described as the training phase of the system. The alphabet is constructed using supervised techniques. That is, the alphabet is constructed using information learned in the training phase to adjust the result according to a statistical algorithm (such as a Viterbi alignment) to arrive at a cost efficient recognition tool. Once such an alphabet is constructed a new set of HMMs can be defined which more accurately reflects parameter typing across writers. The system recognizes handwriting by applying an efficient hierarchical decoding strategy which employs a fast match and a detailed match function, thereby making the recognition cost effective.
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Citations
23 Claims
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1. A method for operating a handwriting recognition system having a programmed digital data processor, comprising the data processor executed steps of:
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generating a sequence of feature vectors in response to a two-dimensional trace associated with the input handwriting; projecting the sequence of feature vectors onto a higher dimensional feature space that represents sub-character sections of the input handwriting; performing a fast match of the sequence of feature vectors by generating a list of probable candidate characters using single-state, hidden Markov models (HMMs) having output probability distributions obtained from writer-independent higher dimensional feature space prototype distributions; and performing a detailed match on the list of probable candidate characters using multi-state, writer independent HMMs to identify a most probable character sequence that the input handwriting represents;
whereinat least one of the steps of performing a fast match and performing a detailed match operate in accordance with a supervision process. - View Dependent Claims (2)
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3. A method for operating a handwriting recognition system having a programmed digital data processor, comprising the steps of:
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generating a sequence of feature vectors in response to a two-dimensional trace associated with the input handwriting; projecting the sequence of feature vectors onto a higher dimensional feature space that represents sub-character sections of the input handwriting; performing a fast match of the sequence of feature vectors by generating a list of probable candidate characters sequentially using single-state, hidden Markov models (HMMs) having output probability distributions obtained from writer-independent higher dimensional feature space prototype distributions; and performing a detailed match on the list of probable candidate characters using multi-state, writer independent HMMs to identify a most probable character sequence that the input handwriting represents; and
further including the initial steps of;tagging individual ones of handwriting feature vectors in accordance with a statistical alignment; constructing a binary clustering tree from the tagged feature vectors; pruning the binary clustering tree in accordance with a predetermined pruning criteria to retain a first number of leaves L1 for forming clusters; further pruning the binary clustering tree to retain a second number of leaves L2, wherein L2<
L1, for forming a plurality of elementary handwriting units representing a writer-independent label alphabet; andestablishing a relationship between the elementary handwriting units and clusters by a step of determining mixture coefficients. - View Dependent Claims (4, 5, 6, 7, 8)
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9. A handwriting recognition system that includes a programmed digital data processor, said digital data processor being programmed to implement a handwriting recognition system that is comprised of:
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means for generating a sequence of feature vectors in response to input handwriting received from a handwriting transducer means; means for projecting the sequence of feature vectors onto a higher dimensional feature space that represents sub-character sections of the input handwriting; means for performing a fast match of the sequence of feature vectors, including means for generating list of probable candidate characters using single-state, hidden Markov models (HMMs) having output probability distributions obtained from writer-independent higher dimensional feature space prototype distributions; and means for performing a detailed match on the list of probable candidate characters using multi-state, writer independent HMMs, said detailed match performing means having an output for expressing a most probable character sequence that the input handwriting represents;
whereinat least one of said means for performing a fast match and said means for performing a detailed match operate in accordance with a supervision process. - View Dependent Claims (10)
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11. A handwriting recognition system that includes a programmed digital data processor, said digital data processor being programmed to implement a handwriting recognition system that is comprised of:
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means for generating a sequence of feature vectors in response to input handwriting received from a handwriting transducer means; means for projecting the sequence of feature vectors onto a higher dimensional feature space that represents sub-character sections of the input handwriting; means for performing a fast match of the sequence of feature vectors, including means for generating a list of probable candidate characters sequentially using single-state, hidden Markov models (HMMs) having output probability distributions obtained from writer-independent higher dimensional feature space prototype distributions; and means for performing a detailed match on the list of probable candidate characters using multi-state, writer independent HMMs, said detailed match performing means having an output for expressing a most probable character sequence that the input handwriting represents; and
further comprising;means for tagging individual ones of handwriting feature vectors in accordance with statistical alignments; means for constructing a binary clustering tree from the tagged feature vectors; means for pruning the binary clustering tree in accordance with a predetermined pruning criteria to retain a first number of leaves L1 for forming clusters; means for further pruning the binary clustering tree to retain a second number of leaves L2, wherein L2 <
L1, for forming a plurality of elementary handwriting units representing a writer-independent label alphabet; andmeans for establishing a relationship between the elementary handwriting units and clusters in accordance with determined mixture coefficients.
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12. A method for operating a handwriting recognition system having a programmed digital data processor, comprising the data processor executed steps of:
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obtaining training data from a plurality of writers; extracting parameter vectors from the obtained training data; determining from the parameter vectors a plurality of allographs, where each allograph represents a different style for writing a character of interest; generating an initial hidden Markov model (HMM) for each allograph; extracting feature vectors from the obtained training data; projecting the feature vectors onto a higher dimensional feature space that represents sub-character sections of the input handwriting; aligning each feature vector against an appropriate one of the HMMs; forming a cluster comprised of all feature vectors that are aligned against the same allograph; determining a statistical description of each cluster; forming a clustering tree in accordance with the statistical descriptions; forming a set of prototypes from the clustering tree; forming a set of elementary handwriting units from the clustering tree; generating a set of HMMs for each allograph based on the formed set of prototypes and the formed set of elementary handwriting units; performing a fast match of the sequence of feature vectors by generating a list of probable candidate characters sequentially using single-state, hidden Markov models (HMMs) having output probability distributions obtained from writer-independent higher dimensional feature space prototype distributions; and performing a detailed match on the list of probable candidate characters using multi-state, writer independent HMMs to identify a most probable character sequence that the input handwriting represents. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
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Specification