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In this paper we present an OCR for Handwritten Devnagari Characters. Basicsymbols are recognized by neural classifier. We have used four featureextraction techniques namely, intersection, shadow feature, chain codehistogram and straight line fitting features. Shadow features are computedglobally for character image while intersection features, chain code histogramfeatures and line fitting features are computed by dividing the character imageinto different segments. Weighted majority voting technique is used forcombining the classification decision obtained from four Multi LayerPerceptron(MLP) based classifier. On experimentation with a dataset of 4900samples the overall recognition rate observed is 92.80% as we considered topfive choices results. This method is compared with other recent methods forHandwritten Devnagari Character Recognition and it has been observed that thisapproach has better success rate than other methods.

Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition