Journal
PATTERN RECOGNITION
Volume 42, Issue 1, Pages 167-182Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2008.05.012
Keywords
Optical character recognition; Chinese handwriting recognition; Sliding window; Hidden Markov Model; Segmentation-free strategy; Classifier combination
Funding
- National Natural Science Foundation of China [60475011]
- Heilongjiang Natural Science Foundation of China [F0322]
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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. Published by Elsevier Ltd.
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