Handwritten Devanagari character recognition using wavelet based feature extraction and classification scheme

This paper gives a new approach for recognition of handwritten Devanagari characters. Twenty handwritten characters from 100 people resulting 2000 characters are used for the experimentation. The handwritten characters written of paper is scanned, preprocessed and on every individual characters wavelet transform is applied so as to get decomposed images of characters. Statistical parameters are computed over the decomposition to form feature vector. The feature vectors serve as input to back propagation neural networks for classification into one of 20 classes and based classes they are recognized. The accuracy obtained is around 70 percent over large number of samples.