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Decision tree classifier for character recognition combining support vector machines and artificial neural networks

: Grafmüller, M.; Beyerer, J.; Kroschel, K.

Postprint urn:nbn:de:0011-n-1437444 (578 KByte PDF)
MD5 Fingerprint: ab58dff34b86eaa4cedb95ca6d0e5b3a
Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 4.11.2010

Schmalz, M.S. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII : 2 August 2010, San Diego, California, USA
Bellingham, WA: SPIE, 2010 (Proceedings of SPIE 7799)
ISBN: 978-0-8194-8295-2
Paper 77990B
Conference "Mathematics of Data/Image Coding, Compression, and Encryption with Applications" <12, 2010, San Diego/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
character recognition; decision tree; neural network; support vector machine

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.