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Moment of Inertia-Based Approach to Recognize Arabic Handwritten Numerals

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Innovations in Electronics and Communication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 65))

Abstract

Simpler methods of feature extraction and better accuracy have always been primary needs of a handwriting recognition system to be a successful real-time system. The novelty of this paper lies in the introduction of two unique methods of feature extraction which are Pixel Moment of Inertia (PMI) and Delta Distance Coding (DDC). PMI absorbs angular variations in the samples and DDC performs précised local curve coding for better recognition accuracy. Multiple Hidden Markov Model (MHMM) has been used to neutralize the effect of two very frequent writing styles of numerals “4” and “7” on their recognition rates. The paper uses MNIST database, and overall recognition accuracy of 99.01% has been achieved.

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Acknowledgements

MNIST database is a universally accepted benchmark database of English or Arabic numerals available on the link “Y. LeCun [MNIST OCR data]”. This database has been used in this proposed work. I am thankful to the authority of Guru Nanak Institutions Technical Campus, Ibrahimpatnam, T.S. India to provide us with Internet and other allied facilities to carry out research works.

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Authors and Affiliations

  1. ECE Department, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    Binod Kumar Prasad

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  1. Binod Kumar Prasad
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Correspondence to Binod Kumar Prasad .

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Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    R. K. Singh

  3. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Girish Kumar

  4. Department of ECE, NIT Srinagar, Srinagar, Jammu and Kashmir, India

    G.M. Rather

  5. Department of ECE, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    K. Santhi

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Prasad, B.K. (2019). Moment of Inertia-Based Approach to Recognize Arabic Handwritten Numerals. In: Saini, H., Singh, R., Kumar, G., Rather, G., Santhi, K. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-13-3765-9_26

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  • DOI: https://doi.org/10.1007/978-981-13-3765-9_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3764-2

  • Online ISBN: 978-981-13-3765-9

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