CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

  • Ahmed El-Sawy
  • Hazem EL-Bakry
  • Mohamed LoeyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 533)


In recent years, handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. The paper provided a deep learning technique that can be effectively apply to recognizing Arabic handwritten digits. LeNet-5, a Convolutional Neural Network (CNN) trained and tested MADBase database (Arabic handwritten digits images) that contain 60000 training and 10000 testing images. A comparison is held amongst the results, and it is shown by the end that the use of CNN was leaded to significant improvements across different machine-learning classification algorithms.


Recognition Rate Deep Learning Convolutional Neural Network Handwritten Digit Digit 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.


  1. 1.
    Selvi, P.P., Meyyappan, T.: Recognition of Arabic numerals with grouping and ungrouping using back propagation neural network. In: International Conference on Proceedings of Pattern Recognition, Informatics and Mobile Engineering (PRIME), pp. 322–327 (2013)Google Scholar
  2. 2.
    Mahmoud, S.: Recognition of writer-independent off-line handwritten Arabic (Indian) numerals using hidden Markov models. Sig. Process. 88(4), 844–857 (2008)CrossRefzbMATHGoogle Scholar
  3. 3.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  4. 4.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc., Red Hook (2012)Google Scholar
  5. 5.
    Afaq Ali Shah, S., Bennamoun, M., Boussaid, F.: Iterative deep learning for image set based face and object recognition. Neurocomputing 174, 866–874 (2016)CrossRefGoogle Scholar
  6. 6.
    Zhang, Q., Yang, L.T., Chen, Z.: Deep computation model for unsupervised feature learning on big data. IEEE Trans. Serv. Comput. 9(1), 161–171 (2016)Google Scholar
  7. 7.
    Chen, X.W., Lin, X.: Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014)CrossRefGoogle Scholar
  8. 8.
    Cai, M., Liu, J.: Maxout neurons for deep convolutional and LSTM neural networks in speech recognition. Speech Commun. 77, 53–64 (2016)CrossRefGoogle Scholar
  9. 9.
    Sainath, T.N., Kingsbury, B., Saon, G., Soltau, H., Mohamed, A.-R., Dahl, G., Ramabhadran, B.: Deep convolutional neural networks for large-scale speech tasks. Neural Netw. 64, 39–48 (2015)CrossRefGoogle Scholar
  10. 10.
    Collobert, R., Weston, J., Bottou, O., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)zbMATHGoogle Scholar
  11. 11.
    Maitra, D.S., Bhattacharya, U., Parui, S.K.: CNN based common approach to handwritten character recognition of multiple scripts. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1021–1025 (2015)Google Scholar
  12. 12.
    Yu, N., Jiao, P., Zheng, Y.: Handwritten digits recognition base on improved LeNet5. In: Proceedings of Control and Decision Conference (CCDC), 2015 27th Chinese, pp. 4871–4875 (2015)Google Scholar
  13. 13.
    Niu, X.-X., Suen, C.Y.: A novel hybrid CNNSVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012)CrossRefGoogle Scholar
  14. 14.
    Tissera, M.D., McDonnell, M.D.: Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174, 42–49 (2016)CrossRefGoogle Scholar
  15. 15.
    Ali, S.S., Ghani, M.U.: Handwritten digit recognition using DCT and HMMs. In: 2014 12th International Conference on Frontiers of Information Technology (FIT), pp. 303–306 (2014)Google Scholar
  16. 16.
    Melhaoui, O.E., Hitmy, M.E., Lekhal, F.: Arabic numerals recognition based on an improved version of the loci characteristic. Int. J. Comput. Appl. 24(1), 36–41 (2011)Google Scholar
  17. 17.
    Mahmoud, S.A.: Arabic (Indian) handwritten digits recognition using Gabor-based features. In: International Conference on Proceedings of Innovations in Information Technology, IIT 2008, pp. 683–687 (2008)Google Scholar
  18. 18.
    Takruri, M., Al-Hmouz, R., Al-Hmouz, A.: A three-level classifier: fuzzy C means, support vector machine and unique pixels for Arabic handwritten digits. In: World Symposium on Proceedings of Computer Applications & Research (WSCAR), pp. 1–5 (2014)Google Scholar
  19. 19.
    Salameh, M.: Arabic digits recognition using statistical analysis for end/conjunction points and fuzzy logic for pattern recognition techniques. World Comput. Sci. Inf. Technol. J. 4(4), 50–56 (2014)Google Scholar
  20. 20.
    Alkhateeb, J.H., Alseid, M.: DBN - based learning for Arabic handwritten digit recognition using DCT features. In: 2014 6th International Conference on Computer Science and Information Technology (CSIT), pp. 222–226 (2014)Google Scholar
  21. 21.
    Hafiz, A.M., Bhat, G.M.: Boosting OCR for some important mutations. In: Second International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 128–132 (2015)Google Scholar
  22. 22.
    Wu, H., Gu, X.: Towards dropout training for convolutional neural networks. Neural Netw. 71, 1–10 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer and Informatics, Computer Science DepartmentBenha UniversityBenhaEgypt
  2. 2.Faculty of Computer and Information Sciences, Information System DepartmentMansoura UniversityMansouraEgypt

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