Skip to main content

Advertisement

SpringerLink
Log in

MG-CNN: Morphological gradient convolutional neural network for classification of arabic styles

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The Arabic language is characterized by a great diversity in its writing styles in terms of the shape, size, and percentage of inclination and methods of drawing. Despite this great diversity, the Arabic styles are also characterized by a huge similarity that makes it difficult for traditional methods of machine learning to overcome with Arabic manuscripts. In this paper we present a Morphological Gradient Convolutional Neural Network to Classify the Arabic styles (MG-CNN). The model is a combination of two methods: a morphological gradient to detect images’ contours and a convolutional neural network to extract images features and classify them. Due to the absence of Arabic styles dataset, we created an image database from the book “Teach Yourself Arabic styles: Naskh, Rokaa, Farissi, Tolot, Diwani” (Mehdi, Teach yourself arabic styles: Naskh, Rokaa, Farissi, Tolot, Diwani, 2005) and then we use augmentation methods to increase number of images while preserving the characteristics of each style. Our architecture gives a high accuracy of 100% for the training dataset and 99.50% for the validation dataset.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Obayd, A., Maha, A.: Types and forms of Arabic styles. mawdoo3(2019). Accessed 23 March (2021)

  3. Al-Yousefi, S., Upda, H.: Recognition of Arabic characters. IEEE Trans. Pattern Anal. Mach. Intell. 14, 853–857 (1992). https://doi.org/10.1109/34.149585

    Article  Google Scholar 

  4. Amin, A.: Off line Arabic character recognition: a survey. Proc. Fourth Int. Conf. Doc. Anal. Recognit. 2, 596–599 (1997). https://doi.org/10.1109/ICDAR.1997.620572

    Article  Google Scholar 

  5. Adam, K., Al-Maadeed, S., Bouridane, A.: Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: preliminary results. In: 2017 1st International Workshop on Arabic Script Analysis, pp 95–98 (2017). https://doi.org/10.1109/ASAR.2017.8067767

  6. Ezz, M., Sharaf, M.A., Hassan, A.A.: Classification of Arabic writing styles in ancient arabic manuscripts. Int. J. Adv. Comput. Sci. Appl. (IJACSA) (2019). https://doi.org/10.14569/IJACSA.2019.0101056

  7. O’shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. IEEE Trans. Cogn. Commun. Netw. 3(4), 563–575 (2017). https://doi.org/10.1109/TCCN.2017.2758370

    Article  Google Scholar 

  8. Aceto, G., Ciuonzo, D., Montieri, A., Pescapé, A.: Mobile encrypted traffic classification using deep learning: experimental evaluation, lessons learned, and challenges. IEEE Trans. Netw. Serv. Manag. 16(2), 445–458 (2019). https://doi.org/10.1109/TNSM.2019.2899085

    Article  Google Scholar 

  9. Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)

    MATH  Google Scholar 

  10. Mehdi, M., Said.: Teach yourself arabic styles: Naskh, Rokaa, Farissi, Tolot, Diwani. AlQahira. Alqahira (2005)

  11. Rivest, J.F., Soille, P., Beucher, S.: Morphological gradients. J. Electron. Imaging 2(4), 326–336 (1993). https://doi.org/10.1117/12.159642

    Article  Google Scholar 

  12. Jufriadif, N., Johan, H., Sarifuddin, M., Eri, P.: The algorithm of image edge detection on panoramic dental X-ray using multiple morphological gradient (mMG) method. Eng. Inf. Technol. Int. J. Adv. Sci. 6, 1012–1018 (2016). https://doi.org/10.18517/ijaseit.6.6.1480

    Article  Google Scholar 

  13. El Atillah, M., El fazazy, K.: Deep morphological gradient for recognition of handwritten digits. Association for computing machinery. In: BDIoT’19: Proceedings of the 4th International Conference on Big Data and Internet of Things 55, 1–5 (2019). https://doi.org/10.1145/3372938.3372993

  14. Google.: Tensorflow. tensorflow (2015). https://www.tensorflow.org/. Accessed 02 June 2021

  15. Yang, J.: ReLU and softmax activation functions. github(2017). https://github.com/Kulbear/deep-learning-nano-foundation/wiki/ReLU-and-Softmax-Activation-Functions. Accessed 02 June 2021

  16. Gholamalinezhad, H., Khosravi, H.: Pooling methods in deep neural networks, a review. CoRR, 2009.07485 (2020). https://arxiv.org/abs/2009.07485

  17. Nagi, J.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 342–347 (2011). https://doi.org/10.1109/ICSIPA.2011.6144164

  18. Shabbeer Basha, S.H., Dubey, S.R., Pulabaigari, V., Mukherjee, S.: Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing 378, 112–119 (2020). https://doi.org/10.1016/j.neucom.2019.10.008

  19. Xujian, F., Jiayi, W., Dewen, S., Binquan, L., Chenxuan, L., Xiyuan, C.: Recommendation algorithm combining ratings and comments. Alex. Eng. J. 60(6), 5009–5018 (2021). https://doi.org/10.1016/j.aej.2021.04.056

    Article  Google Scholar 

  20. Diederik, P., Kingma, J., Lei, B.: ADAM: a method for stochastic optimization. In: 3rd International Conference for Learning Representations. San Diego (2015). https://arxiv.org/abs/1412.6980v9

  21. Jachner, S., van den Boogaar, K.G., Petzoldt, T.: Statistical methods for the qualitative assessment of dynamic models with time delay (R Package qualV). J. Stat. Softw. 22(8), 1–30 (2007)

    Article  Google Scholar 

  22. Chollet, F.: Keras: deep learning for humans. github (2015). https://github.com/keras-team/keras. Accessed 02 June 2021

  23. Shumin, K., Masahiro, T.: Hexpo: A vanishing-proof activation function. Int. Joint Conf. Neural Netw. (IJCNN) 17010651, 2161–4407 (2017). https://doi.org/10.1109/IJCNN.2017.7966168

    Article  Google Scholar 

  24. Hidenori, I., Takio, K.: Improvement of learning for CNN with ReLU activation by sparse regularization. Int. Joint Conf. Neural Netw. (IJCNN) 17010663, 2161–4407 (2017). https://doi.org/10.1109/IJCNN.2017.7966185

    Article  Google Scholar 

  25. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052

    Article  Google Scholar 

  26. Nascita, A., Montieri, A., Aceto, G., Ciuonzo, D., Persico, V., Pescapé, A.: XAI meets mobile traffic classification: understanding and improving multimodal deep learning architectures. IEEE Trans. Network Serv. Manag. 18(4), 4225–4246 (2021). https://doi.org/10.1109/TNSM.2021.3098157

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science, Signals, Automation and Cognitivism Laboratory (LISAC), FSDM, USMBA, Fés, Morocco

    Mouhssine El Atillah, K. El Fazazy, J. Riffi & A. Yahyaouy

Authors
  1. Mouhssine El Atillah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. El Fazazy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Riffi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Yahyaouy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouhssine El Atillah.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Atillah, M., El Fazazy, K., Riffi, J. et al. MG-CNN: Morphological gradient convolutional neural network for classification of arabic styles. SIViP 17, 2259–2266 (2023). https://doi.org/10.1007/s11760-022-02441-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02441-7

Keywords

Search

Navigation