  In this paper we present an OCR for Handwritten Devnagari Characters. Basic
symbols are recognized by neural classifier. We have used four feature
extraction techniques namely, intersection, shadow feature, chain code
histogram and straight line fitting features. Shadow features are computed
globally for character image while intersection features, chain code histogram
features and line fitting features are computed by dividing the character image
into different segments. Weighted majority voting technique is used for
combining the classification decision obtained from four Multi Layer
Perceptron(MLP) based classifier. On experimentation with a dataset of 4900
samples the overall recognition rate observed is 92.80% as we considered top
five choices results. This method is compared with other recent methods for
Handwritten Devnagari Character Recognition and it has been observed that this
approach has better success rate than other methods.
