Publication

Abstract : This paper presents an efficient method for recognizing printed Tamil characters exploring the interclass relationship between them. This is accomplished using Multiclass Hierarchical Support Vector Machines [Crammer et al., 2001; Weston et al., 1998], a new variant of Multi Class Support Vector Machine which constructs a hyperplane that separates each class of data from other classes. 126 unique characters in Tamil language have been identified. A lot of inter-class dependencies were found in them based on their shapes. This enabled the characters to be organized into hierarchies thereby enhancing the process of recognizing the characters. The System was trained using features extracted from the binary character sub-images of sample documents using Hu’s [Hu., 1962; Jain et al., 1996] moment invariant feature extraction method. The system fetched us promising results in comparison with other classifying algorithms like KNN, Bayesian Classifier and decision trees. An accuracy of 96.85% was obtained in the experiments using Multiclass Hierarchical SVM