Abstract
Malayalam character recognition has gained immense popularity in the past few years. The intrinsic challenges present in this domain along with the large character set of Malayalam further complicate the recognition process. Here we present a comparative evaluation of different multiple classifier combination techniques for the offline recognition of Malayalam characters. We have extracted three different features from the preprocessed character images—Density features, Run-length count and Projection profiles. These features are fed as input to three different neural networks and finally the results of these three networks were combined and evaluated using six different classifier combination methods: Max Rule, Sum Rule, Product Rule, Borda Count Rule, Majority Voting and Weighted Majority voting schemes. The best recognition accuracy of 97.67 % was attained using the Weighted Majority scheme considering top 3 results.
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Anitha Mary M.O. Chacko and Dhanya P.M., “A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition”, International Conference on Computational Intelligence in Data Mining (ICCIDM-2014), Springer (2014).
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Chacko, A.M.M.O., Anil Kumar, K.S. (2016). Offline Malayalam Character Recognition: A Comparative Study Using Multiple Classifier Combination Techniques. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 435. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2757-1_8
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DOI: https://doi.org/10.1007/978-81-322-2757-1_8
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