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A novel, generic scheme for off-line handwritten English alphabets characterimages is proposed. The advantage of the technique is that it can be applied ina generic manner to different applications and is expected to perform better inuncertain and noisy environments. The recognition scheme is using a multilayerperceptron(MLP) neural networks. The system was trained and tested on adatabase of 300 samples of handwritten characters. For improved generalizationand to avoid overtraining, the whole available dataset has been divided intotwo subsets: training set and test set. We achieved 99.10% and 94.15% correctrecognition rates on training and test sets respectively. The purposed schemeis robust with respect to various writing styles and size as well as presenceof considerable noise.

Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition