Boosting bonsai trees for handwritten/printed text discrimination
Yann Ricquebourg, Christian Raymond, Baptiste Poirriez, Aurélie Lemaître, Bertrand Coüasnon · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
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.