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Proceedings Paper

Decision tree classifier for character recognition combining support vector machines and artificial neural networks
Author(s): Martin Grafmüller; Jürgen Beyerer; Kristian Kroschel
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Paper Abstract

Since the performance of a character recognition system is mainly determined by the classifier, we introduce one that is especially tailored to our application. Working with 100 different classes, the most important properties of a reliable classifier are a high generalization capability, robustness to noise and classification speed. For this reason, we designed a classifier that is a combination of two types of classifiers, in which the advantages of both are united. The fundamental structure is given by a decision tree that has in its nodes either a support vector machine or an artificial neural network. The performance of this classifier is experimentally proven and the results are compared with both individual classifier types.

Paper Details

Date Published: 8 September 2010
PDF: 8 pages
Proc. SPIE 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII, 77990B (8 September 2010); doi: 10.1117/12.860500
Show Author Affiliations
Martin Grafmüller, Karlsruhe Institute of Technology (Germany)
Jürgen Beyerer, Fraunhofer-Institut für Informations- und Datenverarbeitung (Germany)
Kristian Kroschel, Fraunhofer-Institut für Informations- und Datenverarbeitung (Germany)


Published in SPIE Proceedings Vol. 7799:
Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII
Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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