A modified GA classifier for offline Tamil handwritten character recognition
by Ashlin Deepa Roselent Nelson; Ramisetty Rajeswara Rao
International Journal of Applied Pattern Recognition (IJAPR), Vol. 4, No. 1, 2017

Abstract: In this paper, we proposed a classifier for Tamil handwritten character recognition using skeletonisation and modified GA to improve the recognition results of offline Tamil handwritten characters. The skeletonised character image is traversed from one endpoint to the other in an order, and based on the path of traversal, skeletonisation is explored to generate feature vector. The operations of conventional GA are modified to allow variable string length of chromosomes in GA. Fitness function is computed by integrating the classification capacity of the string metric Levenshtein distance which measures the dissimilarities between two strings. The experimental results on offline Tamil dataset demonstrates that the proposed classifier can automatically minimise the rate of misclassification and also provide better performance compared to GA with the fixed length chromosome. Our algorithm withstands even noisy data. Its comparison with other approaches is also substantiated and results proved that the proposed algorithm exhibits high accuracy in between 85% to 95%.

Online publication date: Sat, 04-Mar-2017

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