Decision Tree Classifier for Character Recognition Combining Support Vector Machines and Artificial Neural Networks
Mathematics of Data/Image Coding, Compression, and Encryption with Applications, Proceedings of SPIE Vol. 7799, 2010.
Image and Signal Processing: Mathematics of Data/Image Coding, Compression, and Encryption with Applications, San Diego, USA, 1.-5. August 2010
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.