An Efficient Candidate Set Size Reduction Method for Coarse-Classification in Chinese Handwriting Recognition

  • Feng-Jun Guo
  • Li-Xin Zhen
  • Yong Ge
  • Yun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4768)


In this paper, we introduce an efficient clustering based coarse-classifier for a Chinese handwriting recognition system to accelerate the recognition procedure. We define a candidate-cluster-number for each character. The defined number indicates the within-class diversity of a character in the feature space. Based on the candidate-cluster-number of each character, we use a candidate-refining module to reduce the size of the candidate set of the coarse-classifier. Experiments show that the method effectively reduces the output set size of the coarse-classifier, while keeping the same coverage probability of the candidate set. The method has a low computation-complexity.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Feng-Jun Guo
    • 1
  • Li-Xin Zhen
    • 1
  • Yong Ge
    • 1
  • Yun Zhang
    • 2
  1. 1.Motorola LabsChina Research CenterShanghaiP.R.C.
  2. 2.Electronic Engineering DepartmentShanghai Jiaotong Univ.ShanghaiP.R.C.

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