HANDWRITTEN CHARACTER RECOGNITION USING FEED-FORWARD NEURAL NETWORK MODELS
Nilay Karade, Manu Pratap Singh, Pradeep K. Butey · 2015
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 of 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. This training set is presented to the six different feed-forward neural networks namely newff, newfit, newpr, newgrnn, newrb and newrbe . The test pattern set is used to evaluate the performance of these neural networks models. The results are compared to find the accuracy in recognition of the respective models. The number of hidden layer, number of neurons in hidden layer, validation checks and gradient factors of the neural networks models are taken into consideration during the training.