US5544264A
Expired / Lapsed
US
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Automatic handwriting recognition using both static and dynamic parameters
Assignee: International Business Machines Corp
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Filing: 1995-05-25
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Granted: 1996-08-06
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Expires: 2015-05-25
Abstract
Abstract Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another. As a result, a combination of these two sources of feature vector information provides a substantial reduction in an overall recognition error rate. Methods to combine probability scores from dynamic and the static character models are also disclosed.
Inventors
- 👤 Jerome R. Bellegarda
- 👤 David Nahamoo
- 👤 Krishna S. Nathan
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 10:59
CPC Classifications
G; G06; G06V; G06V30/00; G06V30/10; G06V30/32; G06V30/36; G06V30/373; G06F; G06F18/00; G06F18/20; G06F18/25; G06F18/254; G06F18/256; G06V30/19; G06V30/191; G06V30/1918; G06V30/333; G06V30/347
Claims Analysis
Total Claims: 6
Claim 1
Independent Claim
123 words
A method for operating a handwriting recognition system, comprising the steps of: receiving, from a handwriting transducer means, x-y coordinate information generated by a writer; extracting temporally-based feature vectors from the x-y coordinate information; extracting spatially-based feature vectors from the x-y coordinate information; and identifying a most probable character (U) from a set of possible characters (a) as being a character that is written by the writer, the step of identifying employing a probabilistic method that considers both the temporally-based feature vectors and the spatially-based feature vectors in accordance with the expression Pr(U|a.sub.j)=Pr(U.sub.t |a.sub.j).sup.1/2 Pr(U.sub.d |a.sub.j)1/2, where Pr(U d |a j ) is determined from the spatially-based feature vectors and where Pr(U t |a j ) is determined from the temporally-based feature vectors.
Claim 2
Independent Claim
123 words
A method for operating a handwriting recognition system, comprising the steps of: receiving, from a handwriting transducer means, x-y coordinate information generated by a writer; extracting temporally-based feature vectors from the x-y coordinate information; extracting spatially-based feature vectors from the x-y coordinate information; and identifying a most probable character (U) from a set of possible characters (a) as being a character that is written by the writer, the step of identifying employing a probabilistic method that considers both the temporally-based feature vectors and the spatially-based feature vectors in accordance with the expression Pr(U|a.sub.j)=Pr(U.sub.t |a.sub.j).sup.1/2 Pr(U.sub.d |a.sub.j)1/2, where Pr(U d |a j ) is determined from the spatially-based feature vectors and where Pr(U t |a j ) is determined from the temporally-based feature vectors.
Claim 3
Independent Claim
177 words
A method for operating a handwriting recognition system to determine if a character written by a writer is a character within a character alphabet (a), comprising the steps of: receiving, from a handwriting transducer means, x-y coordinate information generated by the writer; partitioning the x-y coordinate information into a plurality of frames w t; decoding, using temporally-based feature vectors and temporally-based character models, all frames w t to obtain a first probability Pr(w t |a j ) ∀j, ∀t; decoding, using spatially-based feature vectors and spatially-based character models, the x-y coordinate information associated with a spatial image block s to obtain a second probability Pr(s d |a j ) ∀j; for each image block s, identifying all of the frames w t that lie within that image block; defining a third probability as, ##EQU11## where T is the number of frames of w t within s; and determining a fourth probability that an image block s, given a j , is a most-probable character that is written by the writer in accordance with, Pr(s|a.sub.j)=Pr(s.sub.t |a.sub.j).sup.α Pr(s.sub.d |a.sub.j).sup.1-α.
Claim 4
Independent Claim
177 words
A method for operating a handwriting recognition system to determine if a character written by a writer is a character within a character alphabet (a), comprising the steps of: receiving, from a handwriting transducer means, x-y coordinate information generated by the writer; partitioning the x-y coordinate information into a plurality of frames w t; decoding, using temporally-based feature vectors and temporally-based character models, all frames w t to obtain a first probability Pr(w t |a j ) ∀j, ∀t; decoding, using spatially-based feature vectors and spatially-based character models, the x-y coordinate information associated with a spatial image block s to obtain a second probability Pr(s d |a j ) ∀j; for each image block s, identifying all of the frames w t that lie within that image block; defining a third probability as, ##EQU11## where T is the number of frames of w t within s; and determining a fourth probability that an image block s, given a j , is a most-probable character that is written by the writer in accordance with, Pr(s|a.sub.j)=Pr(s.sub.t |a.sub.j).sup.α Pr(s.sub.d |a.sub.j).sup.1-α.
Claim 5
Dependent Claim
146 words
A handwriting recognition system, comprising: a handwriting transducer means that outputs x-y coordinate information generated by a writer; means, coupled to an output of said transducer means, for extracting temporally-based feature vectors from the x-y coordinate information; means, coupled to said output of said transducer means, for extracting spatially-based feature vectors from the x-y coordinate information; and means, coupled to both said extracting means, for identifying a most probable character (U) from a set of possible characters (a) as being a character that is written by the writer, said means for identifying operating in accordance with a probabilistic method that considers both the temporally-based feature vectors and the spatially-based feature vectors in accordance with the expression Pr(U|a.sub.j)=Pr(U.sub.t |a.sub.j).sup.1/2 Pr(U.sub.d |a.sub.j).sup.1/2, Pr(U d |a j ) is determined from the spatially-based feature vectors and where Pr(U t |a j ) is determined from the temporally-based feature vectors.
Claim 6
Dependent Claim
146 words
A handwriting recognition system, comprising: a handwriting transducer means that outputs x-y coordinate information generated by a writer; means, coupled to an output of said transducer means, for extracting temporally-based feature vectors from the x-y coordinate information; means, coupled to said output of said transducer means, for extracting spatially-based feature vectors from the x-y coordinate information; and means, coupled to both said extracting means, for identifying a most probable character (U) from a set of possible characters (a) as being a character that is written by the writer, said means for identifying operating in accordance with a probabilistic method that considers both the temporally-based feature vectors and the spatially-based feature vectors in accordance with the expression Pr(U|a.sub.j)=Pr(U.sub.t |a.sub.j).sup.1/2 Pr(U.sub.d |a.sub.j).sup.1/2, Pr(U d |a j ) is determined from the spatially-based feature vectors and where Pr(U t |a j ) is determined from the temporally-based feature vectors.
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