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

Authors:
Tong-Hua Su;Tian-Wen Zhang;De-Jun Guan;Hu-Jie Huang
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China
Venue:
Pattern Recognition
Year:
2009

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Abstract

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.