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Statistical Textural Features for Text-Line Level Handwritten Indic Script Identification

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 568))

Abstract

As India is a multilingual country, hence, a variety of scripts are used here to write different languages. However, it becomes essential to recognize a particular script before the selection of an appropriate Optical Character Recognition (OCR) system. The research in this field is comparatively less explored and further research is required, particularly in the field of handwritten documents. This paper presents a robust script identification technique for 11 official handwritten Indic scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Manipuri, Oriya, Tamil, Telugu, Urdu along with Roman script. The recognition is performed at text-line level by using statistical textural features called Neighborhood Gray-Tone Difference Matrix along with Gray-level Run Length Matrix. The proposed method is experimented on a total dataset of 2400 handwritten text-lines of various scripts and yielded an identification rate of 97.69% using Multi Layer Perceptron (MLP) classifier.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, West Bengal, India

    Pawan Kumar Singh, Ram Sarkar & Mita Nasipuri

Authors
  1. Pawan Kumar Singh
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  2. Ram Sarkar
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  3. Mita Nasipuri
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Corresponding author

Correspondence to Pawan Kumar Singh .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Tec, Associate Prof., University of Calcutta A.K. Choudhury School of Information Tec, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Faculty of Computer Science, Bialystok University of Technology Faculty of Computer Science, Bialystok, Poland

    Khalid Saeed

  3. DAIS—Università Ca' Foscari, Full Professor of Computer Science DAIS—Università Ca' Foscari, Mestre, Venice, Venezia, Italy

    Agostino Cortesi

  4. Computer Science & Engineering, University of Calcutta Computer Science & Engineering, Kolkata, West Bengal, India

    Nabendu Chaki

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Singh, P.K., Sarkar, R., Nasipuri, M. (2017). Statistical Textural Features for Text-Line Level Handwritten Indic Script Identification. In: Chaki, R., Saeed, K., Cortesi, A., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 568. Springer, Singapore. https://doi.org/10.1007/978-981-10-3391-9_9

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  • DOI: https://doi.org/10.1007/978-981-10-3391-9_9

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  • Print ISBN: 978-981-10-3390-2

  • Online ISBN: 978-981-10-3391-9

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