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Segmentation-Free Strategy: Basic Algorithms

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Chinese Handwriting Recognition: An Algorithmic Perspective

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

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Abstract

The off-line recognition of realistic Chinese handwriting poses significant challenges. This chapter presents a baseline system for a HMMs-based, segmentation-free strategy to address this problem, in which the character segmentation stage is avoided prior to recognition. Handwritten text lines are first converted to observation sequences using sliding windows. Then an embedded Baum-Welch algorithm is used to train character HMMs. Finally, a posterior best character string maximizing is performed with the help of the Viterbi algorithm. Experiments are conducted on the HIT-MW database, which includes data from 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.

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Authors and Affiliations

  1. Harbin Institute of Technology, Heilongjiang, China

    Tonghua Su

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  1. Tonghua Su
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Su, T. (2013). Segmentation-Free Strategy: Basic Algorithms. In: Chinese Handwriting Recognition: An Algorithmic Perspective. SpringerBriefs in Electrical and Computer Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31812-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-31812-2_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31811-5

  • Online ISBN: 978-3-642-31812-2

  • eBook Packages: EngineeringEngineering (R0)

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