Moment of Inertia-Based Approach to Recognize Arabic Handwritten Numerals

  • Binod Kumar PrasadEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)


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.


Pixel moment of inertia (PMI) Delta distance coding (DDC) Multiple hidden Markov model (MHMM) 



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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.ECE DepartmentGuru Nanak Institutions Technical CampusIbrahimpatnamIndia

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