Performance analysis of handwritten Devnagari and MODI Character Recognition system

For development of offline Handwritten Character Recognition (HWCR) system, scripts have always posed a difficulty. In the proposed system, we present neural and non-neural approach for classification of different characters. After pre-processing, features are extracted using Chain code histogram and Intersection junction techniques. BPN, KNN & SVM have been used to train and classify the Devnagari and MODI vowels separately. The combinations have been compared for both the scripts on the basis of their recognition rate.