Character Recognition Using 2D View and Support Vector Machine

Abstract

This paper proposes Handwritten Character Recognition method using 2D view and Support Vector Machine (SVM). In this all the character images are normalized using line density based nonlinear normalization, which are further used for feature extraction using two dimensional (2D) views. Each character is considered from five different views, and from each view 16 features are extracted and combined to obtain 80 features. Using these features, Radial Basis Function (RBF) of SVM classifier is trained to separate different classes of characters. Handwritten Character database is used for training and testing of SVM classifier. Support Vector Machine is promising recognition method, which is alternative to Neural Network (NN). Experiments show that the proposed method can provide a good recognition result using Support Vector Machines at a recognition rate 82.33%.