Skip to main content
SpringerLink
Log in

Recognition of Tifinagh Characters Using Optimized Convolutional Neural Network

  • Original Paper
  • Published:
Sensing and Imaging Aims and scope Submit manuscript

Abstract

The choice of a convolutional neural network architecture and its parameters to extract optical character recognition is very difficult and tedious. The main objective of this work is to build a very robust and very fast architecture for the recognition of the handwritten characters of the Amazigh language written in Tifinagh character. This work has two optimized architectures, one to recognize RGB images and the other to recognize binary images. Very promising results are obtained compared to other existing systems in terms of precision, recall, F-measure and execution time.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Niharmine, L., Outtaj, B., & Azouaoui, A. (2005). Recognition of handwritten Tifinagh characters using gradient direction features. Journal of Theoretical and Applied Information Technology, 95, 9.

    Google Scholar 

  2. Oujaoura, M., Minaoui, B., Fakir, M., El Ayachi, R., & Bencharef, O. (2014). Recognition of Isolated Printed Tifinagh characters. International Journal of Computers and Applications, 85, 1–13. https://doi.org/10.5120/14802-3005

    Article  Google Scholar 

  3. Wahi, A., Sundaramurthy, S., & Ponnusamy, P. (2014). A comparative study for handwritten tamil character recognition using wavelet transform and Zernike moments. International Journal of Open Information Technologies, 2, 30–35.

    Google Scholar 

  4. Priyadarshni, S. J. S. (2016). Improvement of artificial neural network based character recognition system, using SciLab. Optik, 127, 10510–10518. https://doi.org/10.1016/j.ijleo.2016.05.106

    Article  Google Scholar 

  5. Amara, M., Zidi, K., Zidi, S., & Ghedira, K. (2014). Arabic character recognition based M-SVM: Review. In A. E. Hassanien, M. F. Tolba, & A. A. Taher (Eds.), Advanced machine learning technologies and applications. (pp. 18–25). Cham: Springer. https://doi.org/10.1007/978-3-319-13461-1_3

    Chapter  Google Scholar 

  6. Ouadid, Y., Elbalaoui, A., Boutaounte, M., Fakir, M., & Minaoui, B. (2019). Handwritten tifinagh character recognition using simple geometric shapes and graphs. Indonesian Journal of Electrical Engineering and Computer Science, 13, 598. https://doi.org/10.11591/ijeecs.v13.i2.pp598-605

    Article  Google Scholar 

  7. Sadouk, L., Gadi, T., & Essoufi, E. H. (2017). Handwritten tifinagh character recognition using deep learning architectures. In Proceedings of the 1st international conference on Internet of Things and machine learning-IML ’17 (pp. 1–11). Liverpool: ACM Press. https://doi.org/10.1145/3109761.3109788.

  8. Aharrane, N., Moutaouakil, K. E., & Satori, K. (2015). Recognition of handwritten Amazigh characters based on zoning methods and MLP. WSEAS Transactions on Computers, 14, 9.

    Google Scholar 

  9. Niharmine, L., Outtaj, B., & Azouaoui, A. (2018). Tifinagh handwritten character recognition using genetic algorithms. In 2018 international conference on advanced communication technologies and networking (CommNet) (pp. 1–6). Marrakech: IEEE. https://doi.org/10.1109/COMMNET.2018.8360267.

  10. Benaddy, M., El Meslouhi, O., Es-saady, Y., & Kardouchi, M. (2019). Handwritten Tifinagh characters recognition using deep convolutional neural networks. Sensing and Imaging., 20, 9. https://doi.org/10.1007/s11220-019-0231-5

    Article  Google Scholar 

  11. Es-Saady, Y., Amrouch, M., Rachidi, A., Yassa, M. E., & Mammass, D. (2014). Handwritten Tifinagh character recognition using baselines detection features. International Journal of Scientific and Engineering Research, 5, 7.

    Article  Google Scholar 

  12. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  13. Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167.

  14. Es Saady, Y., Rachidi, A., El Yassa, M., & Mammass, D. (2011). AMHCD: A database for amazigh handwritten character recognition research. International Journal of Computers and Applications, 27, 44–48. https://doi.org/10.5120/3286-4475

    Article  Google Scholar 

  15. Wu, J., Chen, X.-Y., Zhang, H., Xiong, L.-D., Lei, H., & Deng, S.-H. (2019). Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization. Journal of Electronic Science and Technology, 17, 26–40. https://doi.org/10.11989/JEST.1674-862X.80904120

    Article  Google Scholar 

  16. Brochu, E., Cora, V. M., & de Freitas, N. (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599.

  17. Bergstra, J., Yamins, D., & Cox, D. D. (2010). Making a Science of Model Search. Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures., 9, 62.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory LIMATI, Department of Mathematics and Computer Sciences, Polydisciplinary Faculty Beni Mellal, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Biniz

  2. Laboratory TIAD, Department of Computer Sciences, Faculty of Sciences and Technology Beni Mellal, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Rachid El Ayachi

Authors
  1. Mohamed Biniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rachid El Ayachi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Biniz.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Biniz, M., El Ayachi, R. Recognition of Tifinagh Characters Using Optimized Convolutional Neural Network. Sens Imaging 22, 28 (2021). https://doi.org/10.1007/s11220-021-00347-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11220-021-00347-1

Keywords

Search

Navigation