Implementation of Feed-forward Neural Network Models for Pattern Classification Using Transformation Based Feature Extraction Methods

Automatic recognition of handwritten Hindi characters is a difficult and one of the most interesting research areas of pattern recognition field. A lot of work has been done in this area till date; still it is a subject of active research. Hindi characters are cursive in nature and thus characters may be written in various cursive ways. Characters also show a lot of similar features such as header line, vertical bar, curves and etc. Handwritten characters may be of varying sizes, width and orientation, which makes the problem more complicated and difficult to solve. The performance of an optical character recognition system extremely depends on the procedure used to extract quality features from characters. A number of feature extraction, classification and recognition techniques have been used successfully in this area. Proposed work is focused on some of the existing techniques like neural networks for the recognition of handwritten Hindi characters. Neural networks are good at recognizing handwritten characters as these networks are insensitive to the missing data. In this paper, we are implementing and analyzing the performance of feed-forward neural network models to perform pattern classification for handwritten Hindi characters using different transformation based feature extraction methods.