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Proceedings Paper

Boosting bonsai trees for handwritten/printed text discrimination
Author(s): Yann Ricquebourg; Christian Raymond; Baptiste Poirriez; Aurélie Lemaitre; Bertrand Coüasnon
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Paper Abstract

Boosting over decision-stumps proved its efficiency in Natural Language Processing essentially with symbolic features, and its good properties (fast, few and not critical parameters, not sensitive to over-fitting) could be of great interest in the numeric world of pixel images. In this article we investigated the use of boosting over small decision trees, in image classification processing, for the discrimination of handwritten/printed text. Then, we conducted experiments to compare it to usual SVM-based classification revealing convincing results with very close performance, but with faster predictions and behaving far less as a black-box. Those promising results tend to make use of this classifier in more complex recognition tasks like multiclass problems.

Paper Details

Date Published: 24 March 2014
PDF: 12 pages
Proc. SPIE 9021, Document Recognition and Retrieval XXI, 902105 (24 March 2014); doi: 10.1117/12.2042418
Show Author Affiliations
Yann Ricquebourg, Univ. Européenne de Bretagne, IRISA / INSA de Rennes (France)
Christian Raymond, Univ. Européenne de Bretagne, IRISA / INSA de Rennes (France)
Baptiste Poirriez, Univ. Européenne de Bretagne, IRISA / INSA de Rennes (France)
Aurélie Lemaitre, IRISA, Univ. de Rennes 2 (France)
Bertrand Coüasnon, Institut National des Sciences Appliquées de Rennes (France)


Published in SPIE Proceedings Vol. 9021:
Document Recognition and Retrieval XXI
Bertrand Coüasnon; Eric K. Ringger, Editor(s)

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