A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM

  • Hamed Pirsiavash
  • Ramin Mehran
  • Farbod Razzazi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.


Support Vector Machine Natural Language Processing Fuzzy Inference System Support Vector Machine Classifier Multi Layer Perceptrons 
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.


  1. 1.
    Amin, A.: Off line Arabic character recognition - a survey. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 596–599 (1997)Google Scholar
  2. 2.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  3. 3.
    Al-Badr, B., Haralick, R.M.: Segmentation-free word recognition with application to Arabic. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 355–359. IEEE Comput. Soc. Press, Los Alamitos (1995)CrossRefGoogle Scholar
  4. 4.
    Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Transactions on Pattern Analysis & Machine Intelligence 21(6), 495–504 (1999)CrossRefGoogle Scholar
  5. 5.
    Hassin, A.H., Xiang-Long, T., Jia-Feng, L., Wei, Z.: Printed Arabic character recognition using HMM. Journal of Computer Science & Technology 19(4), 538–543 (2004)CrossRefGoogle Scholar
  6. 6.
    Cheung, A., Bennamoun, M., Bergmann, N.W.: An Arabic optical character recognition system using recognition-based segmentation. Pattern Recognition 34(2), 215–233 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Weissman, H., Schenkel, M., Guyon, I., Nohl, C., Henderson, D.: Recognition-based segmentation of on-line run-on handprinted words: input vs. output segmentation. Pattern Recognition 27(3), 405–420 (1994)CrossRefGoogle Scholar
  8. 8.
    Azmi, R., Kabir, E.: A new segmentation technique for ominifont farsi text. Pattern Recognition Letters 22(2), 97–104 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Upper Saddle River (1996)Google Scholar
  10. 10.
    Kavianifar, M., Amin, A.: Preprocessing and structural feature extraction for a multi-fonts Arabic/Persian OCR. In: Conference on Document Analysis and Recognition, pp. 213–216. IEEE Computer Soc., Los Alamitos (1999)Google Scholar
  11. 11.
    Kurdy, B.M., AlSabbagh, M.M.: Omnifont Arabic optical character recognition system. In: Proceedings of Int. Conf. on Information and Communication Technologies: From Theory to Applications, pp. 469–470. IEEE, Piscataway (2004)CrossRefGoogle Scholar
  12. 12.
    Pirsiavash, H., Razzazi, F.: Design and Implementation of a Hierarchical Classifier for Isolated Handwritten Persian/Arabic Characters. In: IJCI Proceedings of International Conference on Signal Processing, Turkey, vol. 1(2) (September 2003)Google Scholar
  13. 13.
    Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IEEE Transactions on Information Theory IT-8, 179–187 (1962)Google Scholar
  14. 14.
    Gonzalez, R.C., Wintz, P.: Digital Image Processing, 2nd edn., pp. 392–423. Addison Wesly Publishing Company, London (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hamed Pirsiavash
    • 1
    • 3
  • Ramin Mehran
    • 2
    • 3
  • Farbod Razzazi
    • 3
  1. 1.Department of Electrical EngineeringSharif University of TechnologyTehranIran
  2. 2.Department of Electrical EngineeringK.N.Toosi Univ. of Tech.TehranIran
  3. 3.Paya Soft co.TehranIran

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