  This work presents the application of weighted majority voting technique for
combination of classification decision obtained from three Multi_Layer
Perceptron(MLP) based classifiers for Recognition of Handwritten Devnagari
characters using three different feature sets. The features used are
intersection, shadow feature and chain code histogram features. Shadow features
are computed globally for character image while intersection features and chain
code histogram features are computed by dividing the character image into
different segments. On experimentation with a dataset of 4900 samples the
overall recognition rate observed is 92.16% 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.
