Application of syntactic properties to three-level recognition of polish hand-written medical texts
Proceedings of the 2006 ACM symposium on Document engineering
Comparison of Feature Reduction Methods in the Text Recognition Task
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Handwriting recognition accuracy improvement by author identification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Application of bidirectional probabilistic character language model in handwritten words recognition
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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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.