Abstract
Handwritten numeral recognition has always been a very challenging task due to many variations in handwritten numerals with different writing styles. It is an active research area now a day. To tackle these variations and to get optimal recognition results, a hybrid feature set, which consists of multiple feature extraction approaches like Box Method, Mean, Standard Deviation and Centre of Gravity, has been used in this paper for recognizing the handwritten numerals. A Neural network has been used for successfully classifying 550 samples taken from “The Chars74” handwritten numerals dataset. The appropriate number of hidden neurons and different membership functions has been used to enhance the recognition results. The proposed recognition system is evaluated and compared with other methods.
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