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Arabic Word Recognition by Classifiers and Context

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Abstract

Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the “best”. In recent years, the combination of different classifiers and the use of contextual information have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme. The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.

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References

  1. Madhvanath S, Govindaraju V. The role of holistic paradigms in handwritten word recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, February 2001, 23(2): 149–164.

    Article  Google Scholar 

  2. Steinherz T, Rivlin E, Intrator N. Off-line cursive script word recognition: A survey. International Journal on Document Analysis and Recognition, IJDAR, 1999, 2(2): 1–33.

    Google Scholar 

  3. Suen C Y. Réflexions sur la reconnaissance de l’écriture cursive. In Proc. CIFED’98, 1er Colloque International Francophone sur l’Ecrit et le Document, Mai 1998, Québec, Canada, pp.1–8.

  4. Impedevo S, Wang P S P, Bunke H (eds.). Automatic bankcheck processing. Series in Machine Perception and Artificial Intelligence, World Scientific, 1997, Vol. 28.

  5. Amin A. Off-line Arabic character recognition: The state of the art. Pattern Recognition, 1998, 31(5): 517–530.

    Google Scholar 

  6. Al Badr B, Mahmoud S A. Survey and bibliography of Arabic optical text recognition. Signal Processing, 1995, 41(1): 49–77.

    Article  Google Scholar 

  7. Essoukhri Ben Amara N. Sur la problématique et les orientations en reconnaissance de l’écriture Arabe. In Proc. CIFED 2002, Hammamet 2002, Tunisia, pp.1–8.

  8. Pechwitz M, Maergner V. HMM based approach for handwritten Arabic word recognition using the IFN/ENIT- database. In Proc. 7th International Conference on Document Analysis and Recognition, (ICDAR), Edinburgh, Scotland, August 2003.

  9. Snoussi-Maddouri S, Amiri H, Belaid A, Choisy C. Combination of local and global vision modeling for Arabic handwritten word recognition. In Proc. International Workshop Frontier in Handwriting IWFHR’02, Ontario, Canada, August 6–8, 2002, pp.128–138.

  10. Souici L, Aoun A, Sellami M. Global recognition system for Arabic literal amounts. In Proc. International Conference on Computer Technologies and Applications, ICCTA’99, August 1999, Alexandria, Egypt.

  11. Farah N, Souici L, Sari T, Sellami M. Knowledge based artificial neural network for Arabic city name recognition. WSEAS Transactions on Systems, April 2004, 3(2): 767–771.

    Google Scholar 

  12. Souici-Meslati L, Sellami M. A hybrid approach for Arabic literal amounts recognition. AJSE, the Arabian Journal of Science and Engineering, October 2004, 29:2B.(2): 177–194.

    Google Scholar 

  13. Xu L, Krzyzak A, Suen C Y. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on systems. Man, and cybernetics, May/June 1992, 22(3): 418–435.

    Google Scholar 

  14. Dengel A, Hoch R, Hones F, Jager T, Malburg M, Weigel A. Techniques for improving OCR results. Handbook of Character Recognition and Document Image Analysis, Bunke H, Wang P S P (eds.), World Scientific, 1997, pp.227–258.

  15. Ruta D, Gabrys B. An overview of classifier fusion methods. Computing and Information Systems, University of Paisley, February 2000, 7(1): 1–10.

    Google Scholar 

  16. Pavlidis T. Algorithms for graphic and image processing. Rockville, MD: {Computer Science Press}, 1982.

    Google Scholar 

  17. Belaid A, Belaid Y. Reconnaissance des formes: Méthodes et applications. {InterEditions}, 1992.

  18. Otsu N. A threshold selection method from gray-level histogram. IEEE Transaction Systems, Man Cybernetics, 1979, 9(1): 62–66.

    Google Scholar 

  19. Ameur A, Romeo-Pakker K, Miled H, Cheriet M. Approche globale pour la reconnaissance de mots manuscrits Arabes. In Proc. CNED’94, 3ème Colloque National sur l’Ecrit et le Document, Juillet 1994, pp.151–156.

  20. Jain A K, Mao J, Mohiuddin K. Artificial neural networks: A tutorial. IEEE Computers, Special Issue on Neural Computing, March 1996, 9(3): 31–44.

    Google Scholar 

  21. Bishop C M. Neural networks for pattern recognition. Clarendon Press, Oxford, 1995.

    Google Scholar 

  22. Singh S, Amin A. Fuzzy Recognition of Chinese Characters. InProc. Irish Machine Vision and Image Processing Conference (IMVIP’99), Dublin, September 8–9, 1999, pp.219–227.

  23. Duin R P W. The combining classifier: To train or not to train? In Proc. 16th International Conference on Pattern Recognition, Quebec City, Canada, August 11–15, 2002, Vol. 2, pp.765–770.

  24. Jain A K, Duin R P W, Mao J. Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, January 2000, 22(1): 4–38.

    Article  Google Scholar 

  25. Kittler J, Hatef M, Duin R P W, Matas J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 1998, 20(3): 226–239.

    Article  Google Scholar 

  26. Paquet T, Lecourtier Y. Recognition of handwritten sentences using a restricted lexicon. Pattern Recognition, March 1993, 26(3): 391–407.

    Article  Google Scholar 

  27. Guillevic D. Unconstrained handwriting recognition applied to the processing of bank cheques [Dissertation]. Computer Science Department, Concordia University, Montreal, Canada, September 1995.

  28. Di Lecce V, Dimauro G, Guerriero A et al. A new hybrid approach for legal amount recognition. In Proc. 7th International Workshop on Frontiers in Handwriting Recognition, IWFHR’2000, Amsterdam, The Netherlands, September 2000, pp.199–208.

  29. Al-Ohali Y, Cheriet M, Suen C Y. Databases for recognition of handwritten Arabic cheques. Pattern Recognition, 2003, 36(1): 111–121.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, Annaba University, BP 12 Annaba, 23000, Algeria

    Nadir Farah, Labiba Souici & Mokhtar Sellami

Authors
  1. Nadir Farah
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  2. Labiba Souici
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  3. Mokhtar Sellami
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Corresponding author

Correspondence to Nadir Farah.

Additional information

Nadir Farah received his B. Eng. and M.Sc. degrees in computer science from Annaba University, Algeria in 1989 and 1994 respectively. Since 1994, he has been a teacher and a researcher in Laboratory of Informatics at Annaba University. Currently, he is a Ph.D. candidate in computer science at the same university. His research interests are pattern recognition, character/word recognition, signal processing and bioinformatics.

Labiba Souici received her B. Eng. and M.Sc. degrees in computer science from Badji Mokhtar University, Annaba, Algeria, in 1992 and 1996 respectively. Since 1996 she has been an assistant professor at the Computer Science Department of the same university. Currently, she is a Ph.D. candidate and a researcher at the LRI Laboratory of Badji Mokhtar University. Her research interests include pattern recognition, Arabic handwritten word recognition, artificial intelligence, hybrid neural systems and perception-oriented systems.

Mokhtar Sellami received his Ph.D. degree in computer sciences from the University of Grenoble (France), in the topics of expert systems and logic programming in 1989. He participates in many international research projects in ESPRIT programme (European Scientific Project Research in Information and Technology) and Algeria . He is currently director of Computer Research Laboratory at Annaba University and a senior lecturer in artificial intelligence and expert systems. His professional interests include pattern recognition applied to Arabic image processing, information technology and knowledge engineering.

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Farah, N., Souici, L. & Sellami, M. Arabic Word Recognition by Classifiers and Context. J Comput Sci Technol 20, 402–410 (2005). https://doi.org/10.1007/s11390-005-0402-9

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  • DOI: https://doi.org/10.1007/s11390-005-0402-9

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