The gradient projection method for the training of hidden Markov models
Speech Communication - Speech science and technology: a selection from the papers presented at the Fourth International Conference in Speech Science and Technology (SST-92)
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Off-line handwritten Chinese character recognition as a compound Bayes decision problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual vector quantization modeling of hand-printed Chinese character recognition
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Offline handwritten Chinese character recognition by radical decomposition
ACM Transactions on Asian Language Information Processing (TALIP)
Markov Random Fields for Handwritten Chinese Character Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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We study a discrete contextual stochastic (CS) model for complex and variant patterns like handwritten Chinese characters. Three fundamental problems of using CS models for character recognition are discussed and several practical techniques for solving these problems are investigated. A formulation for discriminative training of CS model parameters is also introduced and its practical usage investigated. To illustrate the characteristics of the various algorithms, comparative experiments are performed on a recognition task with a vocabulary consisting of 50 pairs of highly similar handwritten Chinese characters. The experimental results confirm the effectiveness of the discriminative training for improving recognition performance.