A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems
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We propose in this paper a new framework to develop a transparent classifier able to deal with reject notions. The generated classifier can be characterized by a strong reliability without loosing good properties in generalization. We show on a musical scores recognition system that this classifier is very well suited to develop a complete document recognition system. Indeed this classifier allows them firstly to extract known symbols in a document (text for example) and secondly to validate segmentation hypotheses. Tests had been successfully performed on musical and digit symbols databases.
KeywordsDocument Recognition Symbol Recognition Musical Score Classification Genetic Algorithm Radial Basis Function Neural Networks Reliability
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