A discriminative linear regression approach to adaptation of multi-prototype based classifiers and its applications for Chinese OCR
Identifieur interne : 000210 ( Main/Exploration ); précédent : 000209; suivant : 000211A discriminative linear regression approach to adaptation of multi-prototype based classifiers and its applications for Chinese OCR
Auteurs : JUN DU [République populaire de Chine] ; QIANG HUO [République populaire de Chine]Source :
- Pattern recognition [ 0031-3203 ] ; 2013.
Descripteurs français
- Pascal (Inist)
- Analyse discriminante, Analyse régression, Régression linéaire, Prototype, Classification automatique, Chinois, Reconnaissance optique caractère, Apprentissage, Erreur de classification minimale, Algorithme, Optimisation, Fonction objectif, Evaluation performance, Classification signal, Reconnaissance forme, Méthode basée sur les marges, Erreur classification minimale.
- Wicri :
- topic : Prototype.
English descriptors
- KwdEn :
- Algorithm, Automatic classification, Chinese, Discriminant analysis, Learning, Linear regression, Margin-based method, Minimum classification error, Objective function, Optical character recognition, Optimization, Pattern recognition, Performance evaluation, Prototype, Regression analysis, Signal classification.
Abstract
This paper presents a new discriminative linear regression approach to adaptation of a discriminatively trained prototype-based classifier for Chinese OCR. A so-called sample separation margin based minimum classification error criterion is used in both classifier training and adaptation, while an Rprop algorithm is used for optimizing the objective function. Formulations for both model-space and feature-space adaptation are presented. The effectiveness of the proposed approach is confirmed by a series of experiments for adaptation of font styles and low-quality text, respectively.
Affiliations:
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Le document en format XML
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A so-called sample separation margin based minimum classification error criterion is used in both classifier training and adaptation, while an Rprop algorithm is used for optimizing the objective function. Formulations for both model-space and feature-space adaptation are presented. The effectiveness of the proposed approach is confirmed by a series of experiments for adaptation of font styles and low-quality text, respectively.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Jun Du" sort="Jun Du" uniqKey="Jun Du" last="Jun Du">JUN DU</name></noRegion><name sortKey="Qiang Huo" sort="Qiang Huo" uniqKey="Qiang Huo" last="Qiang Huo">QIANG HUO</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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