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24 March 2014 Boosting bonsai trees for handwritten/printed text discrimination
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Proceedings Volume 9021, Document Recognition and Retrieval XXI; 902105 (2014)
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yann Ricquebourg, Christian Raymond, Baptiste Poirriez, Aurélie Lemaitre, and Bertrand Coüasnon "Boosting bonsai trees for handwritten/printed text discrimination", Proc. SPIE 9021, Document Recognition and Retrieval XXI, 902105 (24 March 2014);


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