Dr. Sabri Abdullah Mahmoud

Professor
King Fahd University of Petroleum and Minerals, Saudi Arabia


Highest Degree
Ph.D. in Information Systems Engineering from University of Bradford, United Kingdom

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Biography

Dr. Sabri Abdullah Mahmoud is currently working as Professor at King Fahd University of Petroleum & Minerals, Saudi Arabia. He obtained his PhD in Information Systems Engineering from Post Graduate School, University of Bradford, Bradford, UK in 1987. His research interest related to Arabic Document Analysis and Recognition (Document Analysis and Classification, Arabic Text Recognition, Arabic Text Writer Identification/Verification), Arabic Natural Language Processing (Arabic Spell Checking and Correction Identification of Writers of Arabic Documents), Image Analysis (Time-varying Image Analysis, Computer Vision), Applications of Pattern Recognition in other areas. He is professional member of Life Senior Member of IEEE, and the Jordanian Engineers Society. In past he worked as Assistant Professor, Associate Professor at King Saud University, Research and Development Manager at AlManarain Est. for Technical Applications, Riyadh, SA. Applications Systems Consultant at International Systems Engineering Co. (ISE), Riyadh SA, Staff Tutor at Arab Open University, Associate Professor at King Fahad University. He also supervised number of PhD and MSc students. He has published12 papers in journals at national and international level.

Area of Interest:

Computer Sciences
Natural Language Processing
Image Analysis
Computer Vision
Pattern Recognition

Selected Publications

  1. Luqman, H., S.A. Mahmoud and S. Awaida, 2015. Arabic and Farsi font recognition: Survey. Int. J. Pattern Recognit. Artif. Intell., Vol. 29, No. 1. 10.1142/S021800141553002X.
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  2. Mahmoud, S.A., I. Ahmad, W.G. Al-Khatib, M. Alshayeb, M.T. Parvez, V. Margner and G.A. Fink, 2014. KHATT: An open Arabic offline handwritten text database. Pattern Recogn., 47: 1096-1112.
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  3. Luqman, H., S.A. Mahmoud and S. Awaida, 2014. KAFD Arabic font database. Pattern Recogn., 47: 2231-2240.
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  4. Awaida, S.M. and S.A. Mahmoud, 2014. Automatic check digits recognition for arabic using multi-scale features, HMM and SVM classifiers. Br. J. Mathe. Comput. Sci., 4: 2521-2535.
    Direct Link  |  

  5. Parvez, M.T. and S.A. Mahmoud, 2013. Arabic handwriting recognition using structural and syntactic pattern attributes. Pattern Recogn., 46: 141-154.
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  6. Parvez, M. T. and S.A. Mahmoud, 2013. Offline Arabic handwritten text recognition: A survey. ACM Comput. Surveys, Vol. 45, No. 2. 10.1145/2431211.2431222.
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  7. Awaida, S.M. and S.A. Mahmoud, 2013. Writer identification of arabic text using statistical and structural features. Cybernet. Syst. Int. J., 44: 57-76.
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  8. Ahmad, I. and S.A. Mahmoud, 2013. Arabic bank check processing: State of the art. J. Comput. Sci. Technol., 28: 285-299.
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  9. Awaida, S.M. and S.A. Mahmoud, 2012. State of the art in off-line writer identification of handwritten text and survey of writer identification of Arabic text. Educ. Res. Rev., 7: 445-463.
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  10. Ahmed, I., S.A. Mahmoud and M.T. Parvez, 2012. Printed Arabic Text Recognition. In: Guide to OCR for Arabic Scripts, Margner, V. and H. El Abed (Eds.). Chapter 7, Springer, London, UK., ISBN: 978-1-4471-4071-9, pp: 147-168.

  11. Mahmoud, S.A. and W.G. Al-Khatib, 2011. Recognition of Arabic (Indian) bank check digits using log-Gabor filters. Applied Intell., 35: 445-456.
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  12. Alkhalid, A., M. Alshayeb and S.A. Mahmoud, 2011. Software refactoring at the class level using clustering techniques. J. Res. Pract. Inform. Technol., 43: 285-306.
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  13. Alkhalid, A., M. Alshayeb and S. Mahmoud, 2011. Software refactoring at the package level using clustering techniques. IET Software, 5: 276-284.
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  14. Parvez, M.T. and S.A. Mahmoud, 2010. Polygonal approximation of digital planar curves through adaptive optimizations. Pattern Recognit. Lett., 31: 1997-2005.
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  15. Mahmoud, S.A. and M.H. Abu-Amara, 2010. The use of radon transform in handwritten Arabic (Indian) numerals recognition. WSEAS Trans. Comput., 9: 252-267.
    Direct Link  |  

  16. Alkhalid, A., M. Alshayeb and S. Mahmoud, 2010. Software refactoring at the function level using new adaptive K-nearest neighbor algorithm. Adv. Eng. Software, 41: 1160-1178.
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  17. Al-Jamimi, H.A. and S.A. Mahmoud, 2010. Arabic Character Recognition using Gabor Filters. In: Innovations and Advances in Computer Sciences and Engineering, Sobh, T. (Ed.). Springer, Netherlands, ISBN: 978-90-481-3657-5, pp: 113-118.

  18. Al-Hashim, A.G. and S.A. Mahmoud, 2010. Benchmark database and GUI environment for printed Arabic text recognition research. WSEAS Trans. Inform. Sci. Applic., 4: 587-597.
    Direct Link  |  

  19. Mahmoud, S.A. and S.O. Olatunji, 2009. Automatic recognition of off-line handwritten Arabic (Indian) numerals using support vector and extreme learning machines. Int. J. Imaging, 2: 34-53.
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  20. Mahmoud, S.A. and S.M. Awaida, 2009. Recognition of off-line handwritten Arabic (Indian) numerals using multi-scale features and support vector machines vs. hidden Markov models. Arabian J. Sci. Eng., 34: 429-444.
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  21. Mahmoud, S.A. and A.S. Mahmoud, 2009. The use of Hartley transform in OCR with application to printed Arabic character recognition. Pattern Anal. Applic., 12: 353-365.
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  22. Mahmoud, S.A. and A.S. Mahmoud, 2009. Arabic character recognition using Modified Fourier Spectrum (MFS) vs. Fourier descriptors. Cybern. Syst.: Int. J., 40: 189-210.
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  23. Awaidah, S.M. and S.A. Mahmoud, 2009. A multiple feature/resolution scheme to Arabic (Indian) numerals recognition using hidden Markov models. Signal Process., 89: 1176-1184.
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  24. Al-Muhtaseb, H.A., S.A. Mahmoud and R.S. Qahwahi, 2009. A novel minimal script for Arabic text recognition databases and benchmarks. Int. J. Circ. Syst. Signal Process., 3: 145-153.
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  25. Mahmoud, S., 2008. Recognition of writer-independent off-line handwritten Arabic (Indian) numerals using hidden Markov models. Signal Process., 88: 844-857.
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  26. Al-Muhtaseb, H.A., S.A. Mahmoud and R.S. Qahwaji, 2008. Recognition of off-line printed arabic text using hidden markov models. Signal Process., 88: 2902-2912.
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