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Hidden Markov Random Field Based Approach for Off-Line Handwritten Chinese Character Recognition

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Hidden Markov Random Field Based Approach for Off-Line Handwritten Chinese Character Recognition
This paper presents a Hidden Markov Mesh Random Field (HMMRF) based approach for off-line handwritten Chinese characters recognition using statistical observation sequences embedded in the strokes of a character. Due to a large set of Chinese characters and many different writing styles, the recognition of handwritten Chinese characters is very challenging. In our approach, the binary image is first normalized by a nonlinear shape normalization scheme to adjust the width, length, and the correlation of strokes. Two types of stroke-based features are then extracted to represent the observation sequence. The estimation of model parameters and state sequence decoding algorithms are also discussed in the paper. Experimental results on 470 isolated hand-written Chinese characters demonstratethe effectivenessof our approach.
Qing Wang, Rongchun Zhao, Zheru Chi, David Dagan F
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where ICPR
Authors Qing Wang, Rongchun Zhao, Zheru Chi, David Dagan Feng
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