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Model based odia numeral recognition using fuzzy aggregated features

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Abstract

In this paper, an efficient scheme for recognition of handwritten Odia numerals using hidden markov model (HMM) has been proposed. Three different feature vectors for each of the numeral is generated through a polygonal approximation of object contour. Subsequently, aggregated feature vector for each numeral is derived from these three primary feature vectors using a fuzzy inference system. The final feature vector is divided into three levels and interpreted as three different states for HMM. Ten different three-state ergodic hidden markov models (HMMs) are thus constructed corresponding to ten numeral classes and parameters are calculated from these models. For the recognition of a probe numeral, its log-likelihood against these models are computed to decide its class label. The proposed scheme is implemented on a dataset of 2500 handwritten samples and a recognition accuracy of 96.3% has been achieved. The scheme is compared with other competent schemes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769008, India

    Tusar Kanti Mishra, Banshidhar Majhi, Pankaj K. Sa & Sandeep Panda

Authors
  1. Tusar Kanti Mishra
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  2. Banshidhar Majhi
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  3. Pankaj K. Sa
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  4. Sandeep Panda
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Corresponding author

Correspondence to Tusar Kanti Mishra.

Additional information

Tusar Kanti Mishra received his BS degree with honours in computer science and engineering from Biju Patnaik University of Technology, Odisha, India in 2004. He received his MS degree in 2011 and is currently a PhD Candidate in the Department of Computer Science and Engineering at the National Institute of Technology Rourkela, India. His research interests are in the areas of computer vision, image processing and pattern recognition.

Banshidhar Majhi is presently working as a professor in the Department of Computer Science and Engineering, NIT Rourkela. He has 24 years of teaching and research experience and 3 years of industry experience. His research interests include image processing, computer vision, cryptographic protocols and iris biometrics. He has guided 10 doctoral works and published 45 articled in referred journals.

Pankaj K Sa earned his BS degree from Bharathidasan University, India. He has also completed his MS and PhD degrees from National Institute of Technology Rourkela, India, in image processing. His research interest also includes computer vision and computer graphics.

Sandeep Panda is pursuing his BS degree in computer science and engineering at National Institute of Technology Rourkela, India. His research interests include machine learning, pattern recognition and computer vision.

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Mishra, T.K., Majhi, B., Sa, P.K. et al. Model based odia numeral recognition using fuzzy aggregated features. Front. Comput. Sci. 8, 916–922 (2014). https://doi.org/10.1007/s11704-014-3354-9

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  • DOI: https://doi.org/10.1007/s11704-014-3354-9

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