A Learning Pseudo Bayes Discriminant Method Based on Difference Distribution of Feature Vectors

  • Hiroaki Takebe
  • Koji Kurokawa
  • Yutaka Katsuyama
  • Satoshi Naoi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2423)


We developed a learning pseudo Bayes discriminant method, that dynamically adapts a pseudo Bayes discriminant function to a font and image degradation condition present in a text. In this method, the characteristics of character pattern deformations are expressed as a statistic of a difference distribution, and information represented by the difference distribution is integrated into the pseudo Bayes discriminant function. The formulation of integrating the difference distribution into the pseudo Bayes discriminant function results in that a covariance matrix of each category is adjusted based on the difference distribution. We evaluated the proposed method on multi-font texts and degraded texts such as compressed color images and faxed copies. We found that the recognition accuracy of our method for the evaluated texts was much higher than that of conventional methods.


Discriminant Function Recognition Accuracy Character Recognition Difference Distribution Principal Eigenvalue 
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  1. 1.
    R.O. Duda and P.E. Hart, “Pattern Classification and Scene Analysis”, pp.118–121, John Wiley & Sons, Inc., New York, 1973.zbMATHGoogle Scholar
  2. 2.
    G. Nagy and Y. Xu, “Automatic Prototype Extraction for Adaptive OCR”, Proceedings of Fourth International Conference on Document Analysis and Recognition, Ulm, Germany, August, pp.18–20 1997, 278-282.Google Scholar
  3. 3.
    T.K. Ho, “Bootstrapping Text Recognition from Stop Words”, Proceedings of Fourteenth International Conference on Pattern Recognition, Brisbane, Australia, August, pp.17–20, 1998, 605-609.Google Scholar
  4. 4.
    S. Omachi and H. Aso, “A Qualitative Adaptation of Subspace Method for Character Recognition”, Trans. of IEICE(D-II), vol. J82-D-II, No.11, pp.1930–1939, Nov., 1999.Google Scholar
  5. 5.
    S. Omachi, F. Sun, and H. Aso, “A Noise-Adaptive Discriminant Function and Its Application to Blurred Machine Printed Kanji Recognition”, PAMI-22, 3, pp314–319, March 2000.Google Scholar
  6. 6.
    J.H. Friedman, “Regularized Discriminant Analysis”, Journal of American Statistical Association, 84, No.405, pp.165–175, 1989.CrossRefGoogle Scholar
  7. 7.
    S. Tsuruoka, M. Kurita, T. Harada, F. Kimura, and K. Miyake, “Handwritten “KANJI”and “HIRAGANA” Character Recognition Using Weighted Direction Index Histogram Method”, Trans. of IEICE (D), vol.J70-D, No.7 pp.1390–1397 July, 1987.Google Scholar
  8. 8.
    F. Kimura, K. Takashina, S. Tsuruoka, and Y. Miyake, “Modified quadratic discriminant functions and the application to Chinese character recognition”, IEEE Trans. PAMI, vol.9, no.1, pp.149–153, 1987.Google Scholar
  9. 9.
    K. Fujimoto and H. Kamada, “Fast and Precise Character Recognition by Estimating Recognition Probability”, Proceeding of the 1996 Information and Systems Society Conference of IEICE, D-361, Sep. 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hiroaki Takebe
    • 1
  • Koji Kurokawa
    • 1
  • Yutaka Katsuyama
    • 1
  • Satoshi Naoi
    • 1

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