  Classification methods based on learning from examples have been widely
applied to character recognition from the 1990s and have brought forth
significant improvements of recognition accuracies. This class of methods
includes statistical methods, artificial neural networks, support vector
machines (SVM), multiple classifier combination, etc. In this paper, we discuss
the characteristics of the some classification methods that have been
successfully applied to handwritten Devnagari character recognition and results
of SVM and ANNs classification method, applied on Handwritten Devnagari
characters. After preprocessing the character image, we extracted shadow
features, chain code histogram features, view based features and longest run
features. These features are then fed to Neural classifier and in support
vector machine for classification. In neural classifier, we explored three ways
of combining decisions of four MLP's designed for four different features.
