Recognition of Handwritten Characters Based on Wavelet Transform and SVM Classifier

This paper is devoted to the off-line handwritten character recognition based on the two dimensional wavelet transform and a single support vector machine classifier. The wavelet transform provides a representation of the image in independent frequency bands. It performs a local analysis to characterize images of characters in time and scale space. The wavelet transform provides at each level of decomposition four sub-images: a smooth or approximation sub-image and three detail sub-images. In handwritten character recognition, the wavelet transform has received more attention and its performance is related not only to the use of the type of wavelet but also to the type of a sub-image used to provide features. Our objective here is thus to study these two previous points by conducting several tests using several wavelet families and several combinational features derived from sub-images. They show that the symlet wavelet of order 8 is the most efficient and the features derived from the approximation sub-image allow the best discrimination between the handwritten digits.