Cursive-character script recognition using toeplitz model and neural networks

Abstract

This paper presents a hybrid method to use both the idea of projection and Toeplitz Matrix approaches to describe the feature vectors of an image and hence identifying it. The method applies two different tools. The main one is Toeplitz forms and the second is Neural Networks. The image model considered in this work are some selected Arabic scripts. The letter is first projected on 12 axes, then the lengths of these axes are measured and afterwards for the sake of classification and recognition these lengths are compared with the ones in the data base. The method has proved its high efficiency upon the other known approaches. Toeplitz model has shown its successful role in improving the description of the image feature vectors and hence increasing the rate of recognition. The overall algorithm has reached a very low rate of misclassification. Both machine and hand written cases have been studied. In this paper, examples of handwritten scripts are considered.