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

Martin Grafmüller; Jürgen Beyerer; Kristian Kroschel

doi:10.1117/12.860500

7 September 2010 Decision tree classifier for character recognition combining support vector machines and artificial neural networks

Martin Grafmüller, Jürgen Beyerer, Kristian Kroschel

Author Affiliations +

Proceedings Volume 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII; 77990B (2010) https://doi.org/10.1117/12.860500
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

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.

Citation Download Citation

Martin Grafmüller, Jürgen Beyerer, and Kristian Kroschel "Decision tree classifier for character recognition combining support vector machines and artificial neural networks", Proc. SPIE 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII, 77990B (7 September 2010); https://doi.org/10.1117/12.860500

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