OcrV1, Main, Exploration, bibRecord, 000A35

A Cascade Multiple Classifier System for Document Categorization

Identifieur interne : 000A35 ( Main/Exploration ); précédent : 000A34; suivant : 000A36

A Cascade Multiple Classifier System for Document Categorization

Auteurs : Jian-Wu Xu [États-Unis] ; Vartika Singh [États-Unis] ; Venugopal Govindaraju [États-Unis] ; Depankar Neogi [États-Unis]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.

RBID : ISTEX:8748667C19EE86F28A4B26B54A650DEBD9741232

Abstract

Abstract: A novel cascade multiple classifier system (MCS) for document image classification is presented in the paper. It consists of two different classifiers with different feature sets. The proceeding classifier uses image features, learns physical representation of the document, and outputs a set of candidate class labels for the second classifier. The succeeding classifier is a hierarchical classification model based on textual features. The candidate labels set from the first classifier provides subtrees for the second classifier to search in the hierarchical tree and derive a final classification decision. Hence, it reduces the computational complexity and improves classification accuracy for the second classifier. We test the proposed cascade MCS on a large scale set of tax document classification. The experimental results show improvement of classification performance over individual classifiers.

Url:

https://api.istex.fr/document/8748667C19EE86F28A4B26B54A650DEBD9741232/fulltext/pdf

DOI: 10.1007/978-3-642-02326-2_46

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">Abstract: A novel cascade multiple classifier system (MCS) for document image classification is presented in the paper. It consists of two different classifiers with different feature sets. The proceeding classifier uses image features, learns physical representation of the document, and outputs a set of candidate class labels for the second classifier. The succeeding classifier is a hierarchical classification model based on textual features. The candidate labels set from the first classifier provides subtrees for the second classifier to search in the hierarchical tree and derive a final classification decision. Hence, it reduces the computational complexity and improves classification accuracy for the second classifier. We test the proposed cascade MCS on a large scale set of tax document classification. The experimental results show improvement of classification performance over individual classifiers.</div>
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A Cascade Multiple Classifier System for Document Categorization

A Cascade Multiple Classifier System for Document Categorization

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Abstract

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