Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first con...

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Bibliographic Details
Published in:Pattern recognition 2009, Vol.42 (1), p.167-182
Main Authors: Su, Tong-Hua, Zhang, Tian-Wen, Guan, De-Jun, Huang, Hu-Jie
Format: Article
Language:English
Subjects:
Applied sciences
Chinese handwriting recognition
Classifier combination
Coding, codes
Exact sciences and technology
Hidden Markov Model
Information, signal and communications theory
Optical character recognition
Pattern recognition
Segmentation-free strategy
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Sliding window
Telecommunications and information theory
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Summary:Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first converted to observation sequence by sliding windows. Then embedded Baum–Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show the feasibility of such systems and reveal apparent complementary capacities between the segmentation-free systems and the segmentation-based ones.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2008.05.012