Recognition of Handwritten Numerals using RBFSVM Hybrid Model

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for recognizing totally unconstrained handwritten numerals. Due to a great variety of individual writing styles, the problem is very difficult and far from being solved. In this research work, new hybrid classification method is proposed by combining classifiers in a heterogeneous environment using arcing classifier and their performances are analyzed in terms of accuracy. A classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM). Here, modified training sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. Empirical results illustrate that the proposed hybrid systems provide more accurate handwriting recognition system.