Support vector machines in handwritten digits classification

Abstract

In the paper our approach to classify handwritten digits by using support vector machines is described. Because of the unsatisfying, long time of training of SVM we propose to apply k-nearest neighbours algorithm with Manhattan distance to obtain reduced size of training set having a hope that this hybrid method does not make the significantly worse results of recognition. The aim of presented further experiments was to verify this assumption.