Skip to main content
Log in

Abstract

In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition. The performances of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve as a benchmark for future work on the problem. The performance of well known classifiers and feature extraction techniques will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and competes with the state-of-the-art techniques. A total of 54 different classifier/features combinations will be evaluated on Arabic digits in terms of accuracy and classification time. The results are analyzed and the problem of the digit ‘0’ is identified with a proposed method to solve it. Moreover, we propose a strategy to select and design an optimal two-stage system out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy as the highest classifier/features combination but with much less recognition time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. LeCun Y., Bottou L., Bengio Y., Haffner P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  2. Dong, J.: Comparison of algorithms for handwritten numeral recognition. Technical report, CENPARMI, Concordia University, 1999

  3. Liu C.-L., Nakashima K., Sako H., Fujisawa H.: Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognit. 36, 2271–2285 (2003)

    Article  MATH  Google Scholar 

  4. Zhang, P., Bui, T., Suen, C.Y.: Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals. Proc. 8th ICDAR, pp. 136–140, 2005

  5. Teow L., Loe K.: Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognit. 40(6), 1816–1824 (2007)

    Article  Google Scholar 

  6. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI, vol. 24, no. 24, 2002

  7. Lauera F., Suen C., Blocha G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognit. 36, 2271–2285 (2003)

    Article  Google Scholar 

  8. Gorgevik, D., Cakmakov, D.: An efficient three-stage classifier for handwritten digit recognition. Proc. 17th ICPR, pp. 1051–4651 (2004)

  9. Al-Omari F., Al-Jarrah O.: Handwritten Indian numerals recognition system using probabilistic neural networks. Adv. Eng. Inform. 18(1), 9–16 (2004)

    Article  Google Scholar 

  10. Said, F., Yacoub, R., Suen, C.: Recognition of English and Arabic numerals using a dynamic number of hidden neurons. Proc. 5th ICDAR, pp. 237–240, 1999

  11. El-Sherif, E., Abdelazeem, S.: A two-stage system for Arabic handwritten digit recognition tested on a new large database. International Conference on Artificial Intelligence and Pattern Recognition (AIPR-07), Orlando, FL, USA, July 2007, pp. 237–242

  12. Mozaffari, S., Faez, K., Ziaratban, M.: Structural decomposition and statistical description of Farsi/Arabic handwritten numeric characters. Proc. 8th ICDAR, pp. 237–241, 2005

  13. Soltanzadeh H., Rahmati M.: Recognition of Persian handwritten digits using image profiles of multiple orientations. Pattern Recognit. Lett. 25(14), 1569–1576 (2004)

    Article  Google Scholar 

  14. Duda R., Hart P., Strok D.: Pattern Recognition, 2nd edn. Wiley, New York (2000)

    Google Scholar 

  15. Webb A.: Staistical Pattern Recognition, 2nd edn. Wiley, New York (2002)

    Google Scholar 

  16. Furnkranz, J.: Round robin classification. J. Mach. Learn. Res., vol. 2, pp. 721–747, 2002

  17. Friedman, J.: Another approach to polychotomous classification. Technical Report, Stanford University, 1996

  18. Bennett K.P., Mangasarian O.L.: Multicategory discrimination via linear programming. Optim. Methods Softw. 3, 27–39 (1994)

    Article  Google Scholar 

  19. Cortes C., Vapnik V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  20. Gonzales R.C., Woods R.E.: Digital image processing, 2nd edn. Addison-Wesley, Reading, MA, USA (2002)

    Google Scholar 

  21. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognit., vol. 37, 2004

  22. Zhang, P., Bui, T., Suen, C.: Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals. Proc. 8th ICDAR, 2005

  23. Trier O.D., Jain A.K., Taxt T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 641–662 (1996)

    Article  Google Scholar 

  24. Hanmandlu, M., Kumar, H., Mohan, K.: Neural based handwritten character Recognition. Proc 5th, ICDAR, p. 241, 1999

  25. de Oliveira, J., de Carvalho, J., Freitas, C., Sabourin, R.: Feature sets evaluation for handwritten word recognition using a baseline system. Proc. 8th IWFHR, pp. 446–451, 2002

  26. Hsu C., Lin C.: A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 1(13), 415–425 (2002)

    Google Scholar 

  27. Kaynak, C., Alpaydin, E.: MultiStage cascading of multiple classifiers: one man’s noise is another man’s data. Proc. 17th ICML, pp. 455–462, (2000)

  28. Alpaydin, E., Kaynak, C., Alimoglu, F.: Cascading multiple classifiers and representations for optical and pen-based handwritten digit recognition. Proc. 7th IWFHR, pp. 453–462, 2000

  29. Price, D., Knerr, S., Personnaz, L., Dreyfus, G.: Pairwise neural network classifiers with probabilistic outputs. NIPS, pp. 1109–1116, 1995

  30. Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola, A., Bartlett, P., Scholkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers. Cambridge, MA, pp. 61–74, 2000

  31. Davies E.R.: Machine Vision: Theory, Algorithms, Practicalities. Morgan Kaufmann Publishers Inc., San Francisco, CA (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ezzat El-Sherif.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Abdleazeem, S., El-Sherif, E. Arabic handwritten digit recognition. IJDAR 11, 127–141 (2008). https://doi.org/10.1007/s10032-008-0073-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-008-0073-5

Keywords

Navigation