Abstract
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.
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Anquetil, É., Coüasnon, B., Dambreville, F. (2000). A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems. In: Chhabra, A.K., Dori, D. (eds) Graphics Recognition Recent Advances. GREC 1999. Lecture Notes in Computer Science, vol 1941. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40953-X_17
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DOI: https://doi.org/10.1007/3-540-40953-X_17
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