Handwritten digit recognition through wavelet decomposition and wavelet packet decomposition

Handwritten digit recognition is a significant and established problem in computer vision and pattern recognition and a lot of research work has already been carried out in this area. In this paper a new technique for handwritten digit recognition is proposed. As the handwritten digits are not of the same size, thickness, style, position and orientation therefore different challenges have to be faced to resolve the problem of handwritten digit recognition. The uniqueness and variety in the writing styles of different people also influence the pattern and appearance of the digits. Handwritten digit recognition is the method of recognizing and classifying handwritten digits. It has wide application such as automatic processing of bank cheques, postal addresses and tax forms etc. In this paper, we present a wavelets analysis based technique for feature extraction. The task of classification is handled using KNN and SVM classifier. An overall high recognition rate of 97.04 is achieved on the test data set. The proposed scheme is tested on the well known MNIST data set.