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J. Imaging 2018, 4(2), 39; https://doi.org/10.3390/jimaging4020039

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, West Bengal, India
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Received: 15 December 2017 / Revised: 6 February 2018 / Accepted: 8 February 2018 / Published: 13 February 2018
(This article belongs to the Special Issue Document Image Processing)
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Abstract

Script identification is an essential step in document image processing especially when the environment is multi-script/multilingual. Till date researchers have developed several methods for the said problem. For this kind of complex pattern recognition problem, it is always difficult to decide which classifier would be the best choice. Moreover, it is also true that different classifiers offer complementary information about the patterns to be classified. Therefore, combining classifiers, in an intelligent way, can be beneficial compared to using any single classifier. Keeping these facts in mind, in this paper, information provided by one shape based and two texture based features are combined using classifier combination techniques for script recognition (word-level) purpose from the handwritten document images. CMATERdb8.4.1 contains 7200 handwritten word samples belonging to 12 Indic scripts (600 per script) and the database is made freely available at https://code.google.com/p/cmaterdb/. The word samples from the mentioned database are classified based on the confidence scores provided by Multi-Layer Perceptron (MLP) classifier. Major classifier combination techniques including majority voting, Borda count, sum rule, product rule, max rule, Dempster-Shafer (DS) rule of combination and secondary classifiers are evaluated for this pattern recognition problem. Maximum accuracy of 98.45% is achieved with an improvement of 7% over the best performing individual classifier being reported on the validation set. View Full-Text
Keywords: Classifier combination; Dempster-Shafer theory of evidence; Indic script identification; Histograms of Oriented Gradients; Modified Log-Gabor filter transform; Elliptical features Classifier combination; Dempster-Shafer theory of evidence; Indic script identification; Histograms of Oriented Gradients; Modified Log-Gabor filter transform; Elliptical features
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Mukhopadhyay, A.; Singh, P.K.; Sarkar, R.; Nasipuri, M. A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition. J. Imaging 2018, 4, 39.

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