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)

Abstract

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.

Keywords

Discriminant Function Recognition Accuracy Character Recognition Difference Distribution Principal Eigenvalue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hiroaki Takebe
    • 1
  • Koji Kurokawa
    • 1
  • Yutaka Katsuyama
    • 1
  • Satoshi Naoi
    • 1
  1. 1.FUJITSU LABORATORIES LTD.KawasakiJapan

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