Handwritten Digit Recognition by Combining

� Abstract — Recent results in pattern recognition have shown that SVM (Support Vector Machine) classifiers often have superior recognition rates in comparison to other classi- fication methods. In this paper, a cooperation of four SVM classifiers for handwritten digit recognition, each using dif- ferent feature set is examined. We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning. The ob- tained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature sets. In our experiments only one of the cooperation schemes exceeds the recognition rate of a single SVM classifier. However, the classifier cooperation reduces the classifier complexity and need for training samples, decreases classifier training time and sometimes improves the classifier performance.