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Multifont Arabic Characters Recognition Using HoughTransform and Neural Networks

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

Abstract

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.

Keywords

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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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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