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Communication Dans Un Congrès Année : 2016

A Compliant Document Image Classification System based on One-Class Classifier

Résumé

Document image classification in a professional context requires to respect some constraints such as dealing with a large variability of documents and/or number of classes. Whereas most methods deal with all classes at the same time, we answer this problem by presenting a new compliant system based on the specialization of the features and the parametrization of the classifier separately, class per class. We first compute a generalized vector of features based on global image characterization and structural primitives. Then, for each class, the feature vector is specialized by ranking the features according a stability score. Finally, a one-class K-nn classifier is trained using these specific features. Conducted experiments reveal good classification rates, proving the ability of our system to deal with a large range of documents classes.
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Dates et versions

hal-01337132 , version 1 (24-06-2016)

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Citer

Nicolas Sidère, Sabine Barrat, Jean-Yves Ramel, Vincent Poulain D 'Andecy, Saddok Kebairi. A Compliant Document Image Classification System based on One-Class Classifier. 2016 12th IAPR Workshop on Document Analysis Systems (DAS), Apr 2016, Santorini, Greece. pp.96 - 101, ⟨10.1109/DAS.2016.55⟩. ⟨hal-01337132⟩
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