Handwritten digit recognition by combining support vector machines using rule-based reasoning

Abstract

The idea of combining classifiers in order to compensate their individual weakness and to preserve their individual strength has been widely used in pattern recognition applications. The cooperation of two feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers is examined. We investigate the advantages and weaknesses of various decision fusion schemes using rule-based reasoning. The obtained results show that it is difficult to exceed the recognition rate of the classifier applied straightforwardly on the feature families as one set. However, the rule-based cooperation schemes enable an easy and efficient implementation of various rejection criteria that leads to high reliability recognition systems.