Generic Feature Selection and Document Processing

Hassan Chouaib (1) , Nicole Vincent (1) , Florence Cloppet (1) , Salvatore Tabbone (2)

1 CRIP5 - EA 2517 - Centre de Recherche en Informatique de Paris 5
2 QGAR - Querying Graphics through Analysis and Recognition

This paper presents a generic features selection method and its applications on some document analysis problems. The method is based on a genetic algorithm (GA), whose fitness function is defined by combining Adaboot classifiers associated with each feature. Our method is not linked to a classifier achieving the final recognition task; we have used a combination of weak classifiers to evaluate a subset of features. So we select features that can further be used in the most appropriate classifiers. This method has been tested on three applications: Drop caps classification, handwritten digits recognition and text detection. The results show the efficiency and robustness of the proposed approach.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Hassan Chouaib, Nicole Vincent, Florence Cloppet, Salvatore Tabbone. Generic Feature Selection and Document Processing. 10th international Conference on Document Analysis and Recognition - ICDAR 2009, Jul 2009, Barcelone, Spain. pp.356-360. ⟨hal-00589821⟩