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Feature selection based classifier combination approach for handwritten Devanagari numeral recognition

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Abstract

In this paper a method for the recognition of handwritten Hindi numerals is presented. The paper is reporting the effectiveness of the proposed approach, which is utilizing the feature selection based on the Information theory measures. The Multilayer Perceptron (MLP) based classifier combination is used along with feature selection using two criterion functions: (i) Maximum relevance minimum redundancy and (ii) Conditional mutual information maximization. Conditional mutual information based feature selection when driving the ensemble of classifier produces improved recognition results for most of the benchmarking datasets. The improvement is also observed with maximum relevance minimum redundancy based feature selection when used in combination with ensemble of classifiers. The main contribution of the proposed method is that, the method gives quite efficient results utilizing only 10% patterns of the available dataset.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, Institute of Engineering and Technology DAVV, Khandwa Road, Indore, 452017, India

    PRATIBHA SINGH & AJAY VERMA

  2. Computer Science and Engineering Department, Visvesvaraya National Institute of Technology, Nagpur, 440010, India

    NARENDRA S CHAUDHARI

Authors
  1. PRATIBHA SINGH
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  2. AJAY VERMA
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  3. NARENDRA S CHAUDHARI
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Correspondence to PRATIBHA SINGH.

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SINGH, P., VERMA, A. & CHAUDHARI, N.S. Feature selection based classifier combination approach for handwritten Devanagari numeral recognition. Sadhana 40, 1701–1714 (2015). https://doi.org/10.1007/s12046-015-0419-x

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  • DOI: https://doi.org/10.1007/s12046-015-0419-x

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