Publication Type : Conference Paper
Publisher : Control and Telecommunication Technologies
Source : ACT 2009 - International Conference on Advances in Computing, Control and Telecommunication Technologies, Trivandrum, Kerala (2009)
ISBN : 9780769539157
Keywords : Connected component analysis, Document analysis, Existing method, Feature vectors, Font recognition, Gabor filter, Gears, Local minimums, Multilayer neural networks, Optical character recognition, Support vector machines, SVM classifiers, SVM model, Texture analysis, Vectors
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2009
Abstract : Tamil Font Recognition is one of the Challenging tasks in Optical Character Recognition and Document Analysis. Most of the existing methods for font recognition make use of local typographical features and connected component analysis. In this paper, Tamil font recognition is done based on global texture analysis. The main objective of this proposal is to employ support vector machines (SVM) in identifying various fonts in Tamil. The feature vectors are extracted by making use of Gabor filters and the proposed SVM is trained using these features. The method is found to give superior performance over neural networks by avoiding local minima points. The SVM model is formulated tested and the results are presented in this paper. It is observed that this method is content independent and the SVM classifier shows an average accuracy of 92.5%. © 2009 IEEE.
Cite this Research Publication : Dr. Ramanathan R., Ponmathavan, S., Thaneshwaran, L., A. S. Nair, Valliappan, N., and Soman, K. P., “Tamil font recognition using gabor filters and support vector machines”, in ACT 2009 - International Conference on Advances in Computing, Control and Telecommunication Technologies, Trivandrum, Kerala, 2009.