Printed and Handwritten Digits Recognition Using Neural Networks
نویسندگان
چکیده
In this paper, we show a scheme for recognition of handwritten and printed numerals using a multilayer and clustered neural network trained with the backpropagation algorithm. Kirsch masks are adopted for extracting feature vectors and a three-layer clustered neural network with five independent subnetworks is developed for classifying numerals efficiently. The neural network was trainned with a handwritten numeral dabase of more than 9000 patterns of differents writers and differents styles of type. We obtain correct recognition rates of about 96.2 %. Finally, we show how to construct a refinement stage to improve, even more, the results.
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