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General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2011 Vol.3(3): 369-374 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2011.V3.334

An Efficient Character Recognition System for Handwritten Malayalam Characters Based on Intensity Variations

Abdul Rahiman M and Rajasree M S
Abstract—People start learning to read and write during the early stage of education. As years pass by they may have acquired good reading and writing skills. It may not be difficult for them to read any kind of either printed or handwritten characters. Most people have no problem in reading any kind of light prints or heavy prints, upside down prints, prints of different fonts and styles, handwritten whether it is neatly or sloppily written. But Computers may find difficultly in deciphering many kinds of printed characters which is of different fonts and styles or handwritten characters. To evolve a panacea to this problem human brains have been indulging in various research activities. This paper is a humble attempt for the recognition of handwritten Malayalam (a South Indian Language) characters. In our study we have classified the connected characters into 3 categories. Here we propose an algorithm which uses the inveterate characteristic features to recognize these characters with perceptive accuracy by utilizing the intensity variations in the way in which they may be written. This algorithm recognizes the antediluvian script of Malayalam characters which are connected in nature. Here the input is a 24-bit bmp image which can be enscribed using the Light pen. The output is editable version of the recognized Malayalam characters. In our study we have classified the connected characters into 3 categories. The algorithm is tested for 3 sets of samples ranging 402 letters in noiseless environment and produces accuracy of 94%.

Index Terms—Malayalam, Optical character recognition, Feature extraction, Connected character; Intensity variations, HLH patterns.

Abdul Rahiman M, Research Scholar, Karpagam University, Coimbatore. & Asst Professor, Computer Science & Engg, LBS Ins of Tech for Women Trivandrum, Ke rala , India.
Rajasree M S, Professor & Head, Department of Computer Science & Engg, Govt College of Engg Trivandrum, Kerala, India.

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Cite: Abdul Rahiman M and Rajasree M S, "An Efficient Character Recognition System for Handwritten Malayalam Characters Based on Intensity Variations," International Journal of Computer Theory and Engineering vol. 3, no. 3, pp. 369-374, 2011.
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