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

Authors:
Hiroaki Takebe;Koji Kurokawa;Yutaka Katsuyama;Satoshi Naoi
Affiliations:
-;-;-;-
Venue:
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Year:
2002

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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 multifont 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.