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Offline Handwritten Chinese Character Recognition Using Optimal Sampling Features

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Advances in Multimodal Interfaces — ICMI 2000 (ICMI 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1948))

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Abstract

For offline handwritten Chinese character recognition,stroke variation is the most difficult problem to be solved.A new method of optimal sampling features is proposed to compensate for the stroke variations and decrease the within-class pattern variability.In this method,we propose the concept of sampling features based on directional features that are widely used in offline Chinese character recognition.Optimal sampling features are then developed from sampling features by displacing the sampling positions under an optimal criterion.The algorithm for extracting optimal sampling features is proposed.The effectiveness of this method is widely tested using the Tsinghua University database (THCHR).

Supported by 863 Hi-tech Plan (project 863-306-ZT03-03-1)&National Natural Science Foundation of China (project 69972024)

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, State Key Laboratory of Intelligent Technology and Systems Tsinghua University, 100084, Beijing, PRChina

    Rui Zhang & D I N G Xiaoqing

Authors
  1. Rui Zhang
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  2. D I N G Xiaoqing
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Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, P.O.Box 2728, 100080, Beijing, China

    Tieniu Tan

  2. Computer Department, Media Laboratory, Tsinghua University, 100084, Beijing, China

    Yuanchun Shi

  3. Institute of Computing Technology, Chinese Academy of Sciences, P.O.Box 2704, 100080, Beijing, China

    Wen Gao

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© 2000 Springer-Verlag Berlin Heidelberg

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Zhang, R., Xiaoqing, D.I.N.G. (2000). Offline Handwritten Chinese Character Recognition Using Optimal Sampling Features. In: Tan, T., Shi, Y., Gao, W. (eds) Advances in Multimodal Interfaces — ICMI 2000. ICMI 2000. Lecture Notes in Computer Science, vol 1948. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40063-X_60

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  • DOI: https://doi.org/10.1007/3-540-40063-X_60

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41180-2

  • Online ISBN: 978-3-540-40063-9

  • eBook Packages: Springer Book Archive

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