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公开(公告)号：DE69231309D1
公开(公告)日：2000-09-07
申请号：DE69231309
申请日：1992-09-30
Applicant: IBM
Inventor： BELLEGARDA EVELINE JEANNINE , BELLEGARDA JEROME RENE , NAHAMOO DAVID , NATHAN KRISHNA SUNDARAM
Abstract: Method and apparatus for automatic recognition of handwritten text based on a suitable representation of handwriting in one or several feature vector spaces(s), Gaussian modeling in each space, and mixture decoding to take into account the contribution of all relevant prototypes in all spaces. The feature vector space(s) is selected to encompass both a local and a global description of each appropriate point on a pen trajectory. Windowing is performed to capture broad trends in the handwriting, after which a linear transformation is applied to suitably eliminate redundancy. The resulting feature vector space(s) is called chirographic space(s). Gaussian modeling is performed to isolate adequate chirographic prototype distributions in each space, and the mixture coefficients weighting these distributions are trained using a maximum likelihood framework. Decoding can be performed simply and effectively by accumulating the contribution of all relevant prototype distributions. Post-processing using a language model may be included.
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公开(公告)号：DE69231309T2
公开(公告)日：2001-02-15
申请号：DE69231309
申请日：1992-09-30
Applicant: IBM
Inventor： BELLEGARDA EVELINE JEANNINE , BELLEGARDA JEROME RENE , NAHAMOO DAVID , NATHAN KRISHNA SUNDARAM
Abstract: Method and apparatus for automatic recognition of handwritten text based on a suitable representation of handwriting in one or several feature vector spaces(s), Gaussian modeling in each space, and mixture decoding to take into account the contribution of all relevant prototypes in all spaces. The feature vector space(s) is selected to encompass both a local and a global description of each appropriate point on a pen trajectory. Windowing is performed to capture broad trends in the handwriting, after which a linear transformation is applied to suitably eliminate redundancy. The resulting feature vector space(s) is called chirographic space(s). Gaussian modeling is performed to isolate adequate chirographic prototype distributions in each space, and the mixture coefficients weighting these distributions are trained using a maximum likelihood framework. Decoding can be performed simply and effectively by accumulating the contribution of all relevant prototype distributions. Post-processing using a language model may be included.
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公开(公告)号：DE69425412D1
公开(公告)日：2000-09-07
申请号：DE69425412
申请日：1994-10-21
Applicant: IBM
Inventor： BELLEGARDA EVELINE JEANNINE , BELLEGARDA JEROME RENE , NAHAMOO DAVID , NATHAN KRISHNA SUNDARAM
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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公开(公告)号：DE69425412T2
公开(公告)日：2001-03-08
申请号：DE69425412
申请日：1994-10-21
Applicant: IBM
Inventor： BELLEGARDA EVELINE JEANNINE , BELLEGARDA JEROME RENE , NAHAMOO DAVID , NATHAN KRISHNA SUNDARAM
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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