A Novel Hierarchical Classification Scheme for Online Tamil Character Recognition

In this paper we propose a novel three level hierarchical classification scheme for online character recognition for Tamil, a classical Indian language. We make use of the prior knowledge of the writing rules of a Tamil character to build the first level of the classifier for which we outline two methods. The first method utilizes the quantized slope information while the other relies on the trajectory of pen motion for grouping. The number of strokes in the preprocessed character is used for classification at the second level while a k-Nearest Neighbor classifier is employed at the final level. The method that uses the trajectory of the pen motion information is not sensitive to the length of the character and therefore outperforms the method using quantized slope information at the first level of the classifier thereby leading to an increase in the final classification accuracy at the third level from 85% to 96%.