Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition
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A novel, generic scheme for off-line handwritten English alphabets character images is proposed. The advantage of the technique is that it can be applied in a generic manner to different applications and is expected to perform better in uncertain and noisy environments. The recognition scheme is using a multilayer perceptron(MLP) neural networks. The system was trained and tested on a database of 300 samples of handwritten characters. For improved generalization and to avoid overtraining, the whole available dataset has been divided into two subsets: training set and test set. We achieved 99.10 recognition rates on training and test sets respectively. The purposed scheme is robust with respect to various writing styles and size as well as presence of considerable noise.
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