Multifont Arabic Characters Recognition Using HoughTransform and Neural Networks

  • Nadia Ben Amor
  • Najoua Essoukri Ben Amara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Pattern recognition is a well-established field of study and Optical Character Recognition (OCR) has long been seen as one of its important contributions. However, Arabic has been one of the last major languages to receive attention. This paper describes the performance of an approach combining Hough transform in features extraction and Neural Networks in classification. Experimental tests have been carried out on a set of 85.000 samples of characters corresponding to5 different fonts. Some promising experimental results are reported.


Feature Extraction Hide Markov Model Recognition Rate Character Recognition Optical Character Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Amin, A.: Arabic Character Recognition. Handbook of Character Recognition and Document Image Analysis, pp. 397–420. World Scientific Publishing Company, Singapore (1997)Google Scholar
  2. Amor, N.B., Amara, N.: Applying Neural Networks and Wavelet Transform to Multifont Arabic Character Recognition. International Conference on Computing. In: Communications and Control Technologies (CCCT 2004), Austin (Texas), USA, August 14-17 (2004)Google Scholar
  3. Klassen, T.: Towards Neural Network Recognition Of Handwritten Arabic Letters. Dalhousie University (2001)Google Scholar
  4. Lippmann, R.: Pattern Classification using Neural Networks. IEEE Communications Magazine (1989)Google Scholar
  5. Brown, E.W.: Character Recognition by Feature Point Extraction. Northeastern University internal paper (1992)Google Scholar
  6. Amor, N.B., Amara, N.: Hidden Markov Models and Wavelet Transform in Multifont Arabic Characters Recognition. In: International Conference on Computing, Communications and Control Technologies (CCCT 2005), Austin, Texas, USA, Silicon Hills, July 24-27 (2005)Google Scholar
  7. Illingworth, J., Kittler, J.: A Survey of the Hough Transform. Computer Vision, Graphics and Image Processing 44, 87–116 (1988)CrossRefGoogle Scholar
  8. Altuwaijri, M.M., Bayoumi, M.A.: Arabic Text Recognition Using Neural Network. In: ISCAS 1994 IEEE International Symposium on Circuits and systems, vol. 6 (1994)Google Scholar
  9. Amor, N.B., Amara, N.: Multifont Arabic Character Recognition Using Hough Transform and Hidden Markov Models. In: ISPA 2005 IEEE 4th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, September 15-17 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nadia Ben Amor
    • 1
  • Najoua Essoukri Ben Amara
    • 2
  1. 1.Laboratory of Systems and Signal Processing (LSTS)National Engineering School of Tunis (ENIT)Tunisia
  2. 2.Laboratory of Systems and Signal Processing (LSTS)National Engineering School of Sousse (ENISo)Tunisia

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