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• W2005035880 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." @default.
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• W2005035880 date "2009-01-01" @default.
• W2005035880 modified "2023-10-14" @default.
• W2005035880 title "Off-line recognition of realistic Chinese handwriting using segmentation-free strategy" @default.
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• W2005035880 doi "https://doi.org/10.1016/j.patcog.2008.05.012" @default.
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