Method and apparatus for on-line handwriting recognition based on feature vectors that use aggregated observations derived from time-sequential frames
First Claim
1. A method for on-line handwriting recognition based on a hidden Markov model, said method comprising the steps of:
- real-time sensing at least an instantaneous write position of said handwriting;
deriving from said handwriting a time-conforming string of samples, and from said string a series of handwriting intervals each associated by derivation to a handwriting feature vector, including calculating a center of gravity from the samples associated with each handwriting interval, and wherein the feature vectors contain one or more vector elements that are based on local observations derived from a single said handwriting interval, said handwriting interval comprising a handwriting frame, as well as being based on one or more vector elements that are based on aggregated observations derived from time-spaced said intervals defined over an associated delay, wherein the aggregated feature vector elements are representable as o'"'"'1 and o'"'"'2 as follows;
##EQU2## where o1 and o2 represent the vector elements that are based on local observations, d corresponds to a maximum value of the delay between successive frames, and m indicates a number of functions actually used;
matching the time-conforming sequence of feature vectors so derived to one or more example sequence modellings from a database pertaining to the handwriting in question;
selecting from said modellings a sufficiently-matching recognition string through hidden-Markov processing; and
outputting essentials of a result of said selecting, or alternatively, rejecting said handwriting as failing to be recognized.
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Abstract
A method for on-line handwriting recognition is based on a hidden Markov model and implies the following steps: sensing real-time at least an instantaneous write position of the handwriting, deriving from the handwriting a time-conforming string of segments each associated to a handwriting feature vector, matching the time-conforming string to various example strings from a data base pertaining to the handwriting, and selecting from the example strings a best-matching recognition string through hidden-Markov processing, or rejecting the handwriting as unrecognized. In particular, the feature vectors are based on local observations derived from a single segment, as well as on compacted observations derived from time-sequential segments.
62 Citations
18 Claims
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1. A method for on-line handwriting recognition based on a hidden Markov model, said method comprising the steps of:
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real-time sensing at least an instantaneous write position of said handwriting; deriving from said handwriting a time-conforming string of samples, and from said string a series of handwriting intervals each associated by derivation to a handwriting feature vector, including calculating a center of gravity from the samples associated with each handwriting interval, and wherein the feature vectors contain one or more vector elements that are based on local observations derived from a single said handwriting interval, said handwriting interval comprising a handwriting frame, as well as being based on one or more vector elements that are based on aggregated observations derived from time-spaced said intervals defined over an associated delay, wherein the aggregated feature vector elements are representable as o'"'"'1 and o'"'"'2 as follows;
##EQU2## where o1 and o2 represent the vector elements that are based on local observations, d corresponds to a maximum value of the delay between successive frames, and m indicates a number of functions actually used;matching the time-conforming sequence of feature vectors so derived to one or more example sequence modellings from a database pertaining to the handwriting in question; selecting from said modellings a sufficiently-matching recognition string through hidden-Markov processing; and outputting essentials of a result of said selecting, or alternatively, rejecting said handwriting as failing to be recognized. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A data processing system for handwriting recognition comprising:
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a graphical input means for real-time sensing at least an instantaneous write position of handwriting and converting said handwriting information into a handwriting signal; a handwriting processing means for receiving and processing said handwriting signal, for deriving from said handwriting a time-conforming string of samples and from said string a series of handwriting intervals each associated by derivation to a handwriting feature vector, and for calculating a center of gravity from the samples associated with each handwriting interval, and wherein said feature vectors contain one or more vector elements that are based on local observations derived from a single said handwriting interval, said hand writing interval being selected from the group consisting of a handwriting frame and a handwriting segment, as well as being based on one or more vector elements that are based on aggregated observations derived from time-spaced said intervals defined over an associated delay, wherein the aggregated feature vector elements are representable as o'"'"'1 and o'"'"'2 as follows;
##EQU3## where o1 and o2 represent the vector elements that are based on local observations, d corresponds to a maximum value of the delay between successive frames or segments, and m indicates a number of functions actually used;a handwriting model description database pertaining to the handwriting in question; search and classification means for receiving a processed handwriting signal from the handwriting processing means, and for matching the time-conforming sequence of feature vectors so derived to one or more example sequence modeling from the handwriting model description database, and for selecting from said modeling a sufficiently-matching recognition string through hidden-Markov processing; and results output means for outputting essentials of a result of said selecting, or alternatively, for rejecting said handwriting as failing to be recognized. - View Dependent Claims (10)
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11. A method for on-line handwriting recognition based on a hidden Markov model, said method comprising the steps of:
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real-time sensing at least an instantaneous write position of said handwriting; deriving from said handwriting a time-conforming string of samples, and from said string a series of handwriting intervals each associated by derivation to a handwriting feature vector, including calculating a center of gravity from the samples associated with each handwriting interval, and wherein the feature vectors contain one or more vector elements that are based on local observations derived from a single said handwriting interval, said handwriting interval comprising a handwriting segment, as well as being based on one or more vector elements that are based on aggregated observations derived from time-spaced said intervals defined over an associated delay, wherein the aggregated feature vector elements are representable as o'"'"'1 and o'"'"'2 as follows;
##EQU4## where o1 and o2 represent the vector elements that are based on local observations, d corresponds to a maximum value of the delay between successive segments, and m indicates a number of functions actually used;matching the time-conforming sequence of feature vectors so derived to one or more example sequence modellings from a database pertaining to the handwriting in question; selecting from said modellings a sufficiently-matching recognition string through hidden-Markov processing; and outputting essentials of a result of said selecting, or alternatively, rejecting said handwriting as failing to be recognized. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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Specification