A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems
Identifieur interne : 001E02 ( Main/Exploration ); précédent : 001E01; suivant : 001E03A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems
Auteurs : Éric Anquetil [France] ; Bertrand Coüasnon [France] ; Frédéric Dambreville [France]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2000.
Abstract
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.
Url:
DOI: 10.1007/3-540-40953-X_17
Affiliations:
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<front><div type="abstract" xml:lang="en">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.</div>
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