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A Discrete Contextual Stochastic Model for the Offline Recognition of Handwritten Chinese Characters
July 2001 (vol. 23 no. 7)
pp. 774-782

Abstract—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.

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Index Terms:
Offline recognition of handwritten Chinese characters, contextual stochastic model, discriminative training, Markov random field.
Citation:
Yan Xiong, Qiang Huo, Chorkin Chan, "A Discrete Contextual Stochastic Model for the Offline Recognition of Handwritten Chinese Characters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp. 774-782, July 2001, doi:10.1109/34.935851
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