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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)

Abstract

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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References

  1. 1.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2001)zbMATHGoogle Scholar
  2. 2.
    Bradley, R.S., Fayyad, U.: Refining Initial Points for K-means Clustering. In: Proc. 15th Int’l Conf. Machine Learning Madison, Wisconsin, USA, pp. 91–99 (1999)Google Scholar
  3. 3.
    Lloyd, S.P.: Least Squares Quantization in PCM. IEEE Trans. Information Theory 28, 129–137 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. Fifth Berkeley Symp. Math. Statistics and Probability, Berkeley, California, USA, vol. 1, pp. 281–296 (1967)Google Scholar
  5. 5.
    Forgey, E.: Cluster Analysis of Multivariate Data Efficiency vs. Interpretability of Classification. Biometrics 21, 768 (1965)Google Scholar
  6. 6.
    Lin, T.Z., Fan, K.C.: Coarse Classification of On-Line Chinese Characters via Structure Feature-Based Method. Pattern Recognition 27, 1365–1377 (1994)CrossRefGoogle Scholar
  7. 7.
    Lay, S.R., Lee, C.H., Cheng, N.J., Tseng, C.C.: On-Line Chinese Character Recognition with Effective Candidate Radical and Candidate Character Selections. Pattern Recognition 29, 1647–1659 (1996)CrossRefGoogle Scholar
  8. 8.
    Tang, Y.Y., Tu, L.T., Liu, J., Lee, S.W.: Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification. IEEE Trans. PAMI 21, 258–262 (1999)Google Scholar
  9. 9.
    Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. On Comm COM-28(1), 84–95 (1980)CrossRefGoogle Scholar
  10. 10.
    Kanungo, T., Mount, D.M., Netanyahu, N.S.: An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans. PAMI 24, 881–982 (2002)Google Scholar
  11. 11.
    Yang, Y., Velek, O., Nakagawa, M.: Accelerating Large Character Set Recognition using Pivots. In: Proc. 7th ICDAR, Edinburgh, Scotland, vol. 4C, pp. 262–267 (2003)Google Scholar

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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