7 September 2010 Decision tree classifier for character recognition combining support vector machines and artificial neural networks
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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.
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Martin Grafmüller, Jürgen Beyerer, 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); doi: 10.1117/12.860500; https://doi.org/10.1117/12.860500
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