Research Article | | Peer-Reviewed

Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm

Received: 19 September 2016     Accepted: 14 December 2016     Published: 10 January 2017
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Abstract

Online text recognition systems have been continually given due importance these days globally because of the rapidly developing touch screen gadgets. However, it has been difficult to utilize keyboards and external mouse-like inputs in significantly tinier devices which consequently paved the way to current researched based scientists to look for some newer techniques which could design such type of online systems which could further deal with different kinds of texts for example, digits, symbols and alphabets. In the present paper, an online system for recognizing manually written Arabic numerals is being given. This paper will show digit acquisition, preprocessing, feature extraction and recognition phases in detail. The set of the data was gathered from 100 writers using a touch screen PC with 100 samples of every digit. The average accuracy rate of the outcome of the test of this proposed system was 98%, which is a significant accuracy rate.

Published in International Journal of Data Science and Analysis (Volume 2, Issue 2)
DOI 10.11648/j.ijdsa.20160202.14
Page(s) 37-41
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Arabic Numerals, Handwriting Recognition, Handwritten Numerals, Matching Alignment Algorithm

References
[1] M. A. Abuzaraida, A. M. Zeki and A. M. Zeki, "Recognition Techniques for Online Arabic Handwriting Recognition Systems," In Proceeding of the International Conference on Advanced Computer Science Applications and Technologies (ACSAT2012), Kuala Lumpur, Malaysia, 2012.
[2] Mustafa Ali Abuzaraida, Akram M Zeki and Ahmed M Zeki, "Online Recognition System for Handwritten Hindi Digits Based on Matching Alignment Algorithm," In Proceeding of the Third International Conference on Advanced Computer Science Applications and Technologies (ACSAT2014), Amman, Jordan, 2014.
[3] Mustafa Ali Abuzaraida, Akram M. Zeki and Ahmed M. Zeki, "Problems of writing on digital surfaces in online handwriting recognition systems," In Proceeding of the Information and Communication Technology for the Muslim World (ICT4M), 2013 5th International Conference on, 2013, pp. 1-5.
[4] M. A. Abuzaraida, A. M. Zeki and A. M. Zeki, "Segmentation Techniques for Online Arabic Handwriting Recognition: A survey," In Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World: ICT Connecting Cultures, ICT4M 2010, Jakarta, Indonesia, 2010, pp. D37-D40.
[5] R. Kaplan and E. Kaplan, The Nothing that Is: A Natural History of Zero: Oxford University Press, 1999.
[6] Solomon Gandz, "The Origin of the Ghubār Numerals, or the Arabian Abacus and the Articuli." vol. 16, T. U. o. C. Press, Ed., ed: The University of Chicago Press, pp. 393-424, 1931.
[7] Mustafa Ali Abuzaraida, Akram M Zeki and Ahmed M Zeki, "Online Database of Quranic Handwritten Words," Journal of Theoretical & Applied Information Technology, vol. 62, 2014.
[8] Mustafa Ali Abuzaraida, Akram M Zeki, Ahmed M Zeki and Nor Farahidah Za'bah, "Online Recognition System for Handwritten Arabic Chemical Symbols," In Proceeding of the Computer and Communication Engineering (ICCCE), 2014 International Conference on, 2014, pp. 138-141.
[9] M. A. Abuzaraida, A. M. Zeki and A. M. Zeki, "Difficulties and Challenges of Recognizing Arabic Text," in Computer Applications: Theories and Applications, ed Kuala Lumpur: IIUM Press Malaysia, 2011.
[10] N. Tagougui, M. Kherallah and A. M. Alimi, "Online Arabic handwriting recognition: a survey," International Journal on Document Analysis and Recognition, pp. 1-18, 2012.
[11] Mai Al-Ammar, Reham Al-Majed and Hatim Aboalsamh, "Online Handwriting Recognition for the Arabic Letter Set," Recent Researches in Communications and IT, 2011.
[12] Loader Clive, Local Regression and Likelihood vol. 47: springer New York, 1999.
[13] Douglas David and Peucker Thomas, "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature," Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 10, pp. 112-122, 1973.
[14] Mustafa Ali Abuzaraida, Salem Meftah Jebriel, "The detection of the suitable reduction value of Douglas-Peucker algorithm in online handwritten recognition systems". IEEE International Conference on Service Operations And Logistics, And Informatics (SOLI), Hammamet, Tunisia, 2015. pp 82-87.
[15] M. A. Abuzaraida, Akram M Zeki and Ahmed M Zeki, "Feature Extraction Techniques of Online Handwriting Arabic Text Recognition," In Proceeding of the 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), 2013, pp. 1-7.
[16] Freeman Herbert, "Computer Processing of Line-Drawing Images," ACM Comput. Surv., vol. 6, pp. 57-97, 1974.
[17] R Durbin, S Wddy, A Korgh and G Mitchison, Biological sequence analysis: probabilistic models of proteins and nucleic acids: Cambridge University Press, 1998.
[18] Neil C. Jones and Pavel A. Pevzner, An Introduction to Bioinformatics Algorithms, illustrated ed. Cambridge, Massachusetts London, England: Massachusetts Institute of Technology Press, 2004.
Cite This Article
  • APA Style

    Mustafa Ali Abuzaraida, Akram M. Zeki, Ahmed M. Zeki. (2017). Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm. International Journal of Data Science and Analysis, 2(2), 37-41. https://doi.org/10.11648/j.ijdsa.20160202.14

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    ACS Style

    Mustafa Ali Abuzaraida; Akram M. Zeki; Ahmed M. Zeki. Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm. Int. J. Data Sci. Anal. 2017, 2(2), 37-41. doi: 10.11648/j.ijdsa.20160202.14

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    AMA Style

    Mustafa Ali Abuzaraida, Akram M. Zeki, Ahmed M. Zeki. Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm. Int J Data Sci Anal. 2017;2(2):37-41. doi: 10.11648/j.ijdsa.20160202.14

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  • @article{10.11648/j.ijdsa.20160202.14,
      author = {Mustafa Ali Abuzaraida and Akram M. Zeki and Ahmed M. Zeki},
      title = {Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm},
      journal = {International Journal of Data Science and Analysis},
      volume = {2},
      number = {2},
      pages = {37-41},
      doi = {10.11648/j.ijdsa.20160202.14},
      url = {https://doi.org/10.11648/j.ijdsa.20160202.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20160202.14},
      abstract = {Online text recognition systems have been continually given due importance these days globally because of the rapidly developing touch screen gadgets. However, it has been difficult to utilize keyboards and external mouse-like inputs in significantly tinier devices which consequently paved the way to current researched based scientists to look for some newer techniques which could design such type of online systems which could further deal with different kinds of texts for example, digits, symbols and alphabets. In the present paper, an online system for recognizing manually written Arabic numerals is being given. This paper will show digit acquisition, preprocessing, feature extraction and recognition phases in detail. The set of the data was gathered from 100 writers using a touch screen PC with 100 samples of every digit. The average accuracy rate of the outcome of the test of this proposed system was 98%, which is a significant accuracy rate.},
     year = {2017}
    }
    

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    T1  - Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm
    AU  - Mustafa Ali Abuzaraida
    AU  - Akram M. Zeki
    AU  - Ahmed M. Zeki
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    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    PB  - Science Publishing Group
    SN  - 2575-1891
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    AB  - Online text recognition systems have been continually given due importance these days globally because of the rapidly developing touch screen gadgets. However, it has been difficult to utilize keyboards and external mouse-like inputs in significantly tinier devices which consequently paved the way to current researched based scientists to look for some newer techniques which could design such type of online systems which could further deal with different kinds of texts for example, digits, symbols and alphabets. In the present paper, an online system for recognizing manually written Arabic numerals is being given. This paper will show digit acquisition, preprocessing, feature extraction and recognition phases in detail. The set of the data was gathered from 100 writers using a touch screen PC with 100 samples of every digit. The average accuracy rate of the outcome of the test of this proposed system was 98%, which is a significant accuracy rate.
    VL  - 2
    IS  - 2
    ER  - 

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Author Information
  • Computer Science Department, Faculty of Information Technology, Misurata University, Misurata, Libya

  • Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia

  • Department of Information Systems, College of Information Technology University of Bahrain, Sakhir, Kingdom of Bahrain

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