Combining character classifiers using member classifiers assessment

Abstract

In the paper, the method of combining character classifiers for handprinted text recognition is presented. The combination rule is based on member classifiers reliability assessment. The assessment can be based on probabilistic classifier properties or it can use similarity measures individually evaluated for the character currently being recognized. The approach presented here follows soft classification paradigm, where the classifier not merely selects single class, but it provides the vector of support values corresponding to character likelihood. The proposed methods have been tested and compared in recognizing letters from polish alphabet, including nine difficult do recognize diacritic characters.