METHODS OF COMBINING SVMs APPLIED TO HANDWRITTEN DIGIT RECOGNITION

In this paper, the cooperation of four feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers is examined. We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning. Although most of presented cooperation schemes are variations and adaptations of existing ones, such an extensive number of investigated classifier decision fusion schemes have not been presented in the literature until now. The obtained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature families by combining the individual classifier decisions. In our experiments only one of the cooperation schemes managed to exceed the recognition rate of a single SVM classifier. However, using classifier cooperation reduces the classifier complexity and need for training samples, decreases classifier training time and sometimes improves the classifier performance.