Performance analysis of hybrid feature extraction technique for recognizing English handwritten characters

Abstract

In this paper, an off-line handwritten English character recognition system using hybrid feature extraction technique and neural network classifiers are proposed. A hybrid feature extraction method combines the diagonal and directional based features. The proposed system suitably combines the salient features of the handwritten characters to enhance the recognition accuracy. Neural Network (NN) topologies, namely, back propagation neural network and radial basis function network are built to classify the characters. The k-nearest neighbour network is also built for comparison. The Feed forward NN topology exhibits the highest recognition accuracy and is identified to be the most suitable classifier. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper.