Title: A modified GA classifier for offline Tamil handwritten character recognition

Authors: Ashlin Deepa Roselent Nelson; Ramisetty Rajeswara Rao

Addresses: Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Kukatpally, Hyderabad, Telangana, 500090, India ' Computer Science and Engineering, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh, India

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%.

Keywords: genetic algorithms; mutation; crossover; offline handwritten characters; skeletonisation; tracing; endpoints; intersections; classification; Levenshtein distance; Tamil characters; handwritten characters; character recognition.

DOI: 10.1504/IJAPR.2017.082670

International Journal of Applied Pattern Recognition, 2017 Vol.4 No.1, pp.89 - 105

Received: 12 Mar 2016
Accepted: 09 Oct 2016
Published online: 04 Mar 2017 *

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