Handwriting Recognition using Neural Networks

Author(s):

Shrushti G. Shah , father agnel conceicao rodrigues college of engg ; Kapil Jain, father agnel conceicao rodrigues college of engg

Keywords:

Character recognition, neural network, Back-propogation, Feature extraction, Segmentation

Abstract:

Neural computing is a very extensive, separate science. We apply its solid theory has made it possible to solve many kinds of problems one of which is offline handwriting character recognition. The goal of optical character recognition is to classify the alphanumeric samples of text saved as digital images. We apply the methods of preprocessing, segmentation, feature extraction to enable the input characters into the neural network. The network is later trained using the backpropogation and the radial basis function method. The ultimate goal of conducting the procedure is to develop a system that efficiently recognizes handwritten characters with utmost precision and an optimal learning rate.

Other Details:

Manuscript Id	:	IJSTEV4I2050
Published in	:	Volume : 4, Issue : 2
Publication Date	:	01/09/2017
Page(s)	:	86-89

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