HANDWRITTEN CHARACTER RECOGNITION USING FEED-FORWARD NEURAL NETWORK MODELS

Handwritten character recognition has been vigorous and tough task in the field of pattern recognition. Considering its application to various fields, a lot of work is done and is being continuing to improve the results through various methods. In this paper we have proposed a system for individual handwritten character recognition using multilayer feed-forward neural networks. For the experimental purpose we have taken 15 samples o f lower & upper case handwritten English alphabets in scanned image format i.e. 780 different handwritten character samples. There are two methods of feature extraction are used to construct the pattern vectors for training set.