Recognition of Tamil Handwritten Characters using Daubechies Wavelet Transforms and Feed-Forward Backpropagation Network

Abstract

This article suggests wavelet transform based feature extraction technique for extracting robust features from Tamil handwritten characters. The algorithm uses feed-forward back propagation neural network as the classifier. It presents the relevant features of Tamil script and describes various techniques used for character recognition. In common, all the pattern recognition tasks focus on extracting more differentiating features. This is the most important and complicated job. The proposed system concentrates on two dimensional discrete wavelet transformations for extraction of features. For multiresolution analysis of images, Wavelet Transform is used. This capability can be used to study the character image in different frequency bands. Localized basis functions of WT are used for extracting localized features of a character image. This enables us to obtain more distinct traits as features for each character. Feed forward back propagation neural network is one of the general neural network architectures and this architecture can be applied to many different tasks and is very popular.