Handwritten Hindi character recognition using k-means clustering and SVM

The script `Devanagari´ is used in many Indian languages. Hindi language is also under Devanagari script. In this paper recognition of Hindi characters is done by using a three step procedure. First step is preprocessing, in which binarization of the image and separations of characters are performed. Each Hindi word has a horizontal bar on the top of word. That bar is also removed in preprocessing phase. The next step is feature extraction in which region based k-means clustering is used and the feature vector is created and used in classification phase as input. Third step is classification process, for which support vector machine in used. Support vector machine uses hyper-plane for classification. This hyper-plane is used as a decision surface which is with maximum margin of separation of hyper-plane and closest data point. Support vector machine uses a different kernel functions which defines the way of classification. The kernel function used in Support vector machine for classification is linear kernel function.