Hand Printed Character Recognition Using Neural Networks

In this paper an attempt is made to recognize hand-printed characters by using features extracted using the proposed sector approach. In this approach, the normalized and thinned character image is divided into sectors with each sector covering a fixed angle. The features totaling 32 include vector distances, angles, occupancy and end-points. For recognition, both neural networks and fuzzy logic techniques are adopted. The proposed approach is implemented and tested on hand-printed isolated character database consisting of English characters, digits and some of the keyboard special characters

A Novel Approach to Recognition of English Characters Using Artificial Neural Network

ABTRACT: This paper presents an Artificial Neural Network (ANN) based approach for the recognition of English characters in the presence of noise. Noise has been regarded as one of the major issue that degrades the performance of character recognition system. In order to overcome this limitation, the back propagation (BP based ANN is designed for the English character recognition in presence of noise. The recognition system is designed and tested in MATLAB under different noise levels. Experimental results indicate that the proposed approach can obtain very high recognition rate for all English alphabets in the presence of noise