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Generic Feature Selection and Document Processing

Identifieur interne : 003459 ( Main/Exploration ); précédent : 003458; suivant : 003460

Generic Feature Selection and Document Processing

Auteurs : Hassan Chouaib [France] ; Nicole Vincent [France] ; Florence Cloppet [France] ; Salvatore Tabbone [France]

Source :

RBID : Hal:hal-00589821

Abstract

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.

Url:


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<div type="abstract" xml:lang="en">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.</div>
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