Comparing Feature Extraction Techniques and Classifiers in the Handwritten Letters Classification Problem
Identifieur interne : 000732 ( Main/Exploration ); précédent : 000731; suivant : 000733Comparing Feature Extraction Techniques and Classifiers in the Handwritten Letters Classification Problem
Auteurs : Antonio García-Manso [Espagne] ; J. García-Orellana [Espagne] ; M. González-Velasco [Espagne] ; Miguel Macías-Macías [Espagne] ; Ram N Gallardo-Caballero [Espagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: The aim of this study is to compare the performance of two feature extraction techniques, Independent Component Analysis (ICA) and Principal Component Analysis (PCA) and also to compare two different kinds of classifiers, Neural Networks and Support Vector Machine (SVM). To this aim, a system for handwritten letters recognition was developed, which consist of two stages: a feature extraction stage using either ICA or PCA, and a classifier based on neural networks or SVM. To test the performance of the system, the subset of uppercase letters of the NIST#19 database was used. From the results of our tests, it can be concluded that when a neural network is used as classifier, the results are very similar with the two feature extraction techniques (ICA and PCA). But when the SVM classifier is used, the results are quite different, performing better the feature extractor based on ICA.
Url:
DOI: 10.1007/978-3-642-15825-4_12
Affiliations:
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To this aim, a system for handwritten letters recognition was developed, which consist of two stages: a feature extraction stage using either ICA or PCA, and a classifier based on neural networks or SVM. To test the performance of the system, the subset of uppercase letters of the NIST#19 database was used. From the results of our tests, it can be concluded that when a neural network is used as classifier, the results are very similar with the two feature extraction techniques (ICA and PCA). But when the SVM classifier is used, the results are quite different, performing better the feature extractor based on ICA.</div></front></TEI><affiliations><list><country><li>Espagne</li></country></list><tree><country name="Espagne"><noRegion><name sortKey="Garcia Manso, Antonio" sort="Garcia Manso, Antonio" uniqKey="Garcia Manso A" first="Antonio" last="García-Manso">Antonio García-Manso</name></noRegion><name sortKey="Gallardo Caballero, Ram N" sort="Gallardo Caballero, Ram N" uniqKey="Gallardo Caballero R" first="Ram N" last="Gallardo-Caballero">Ram N Gallardo-Caballero</name><name sortKey="Garcia Manso, Antonio" sort="Garcia Manso, Antonio" uniqKey="Garcia Manso A" first="Antonio" last="García-Manso">Antonio García-Manso</name><name sortKey="Garcia Orellana, J" sort="Garcia Orellana, J" uniqKey="Garcia Orellana J" first="J." last="García-Orellana">J. García-Orellana</name><name sortKey="Gonzalez Velasco, M" sort="Gonzalez Velasco, M" uniqKey="Gonzalez Velasco M" first="M." last="González-Velasco">M. González-Velasco</name><name sortKey="Macias Macias, Miguel" sort="Macias Macias, Miguel" uniqKey="Macias Macias M" first="Miguel" last="Macías-Macías">Miguel Macías-Macías</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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