Abstract
Automatic handwriting recognition is a useful task for many applications. The main Research has focused on the Latin languages. However, few approaches have been proposed for the Arabic language due to the specific and complex features of handwritten Arabic text. In this paper, we propose a Deep Learning (DL) approach for Arabic character recognition using proposed model of convolutional neural networks (CNN). In our work, we dealt with the specific features of Arabic text, in particular the variation of the shape of characters according to its position in the word based a new model of CNN network. In the experimental evaluation, we use hijja dataset in train and test steps. Obtained results prove the efficiency of our model, achieving accuracy of 95% on the Hijja dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Hijja is available at https://github.com/israksu/Hijja2.
References
Bellagha, M.L., Zrigui, M.: Using the MGB-2 challenge data for creating a new multimodal Dataset for speaker role recognition in Arabic TV broadcasts. KES Procedia Comput. Sci. 192, 59–68 (2021)
Mahmoud, A., Zrigui, M.: Distributional semantic model based on convolutional neural network for Arabic textual similarity. Int. J. Cogn. Inform. Nat. Intell. 14(1), 35–50 (2020)
Meddeb, O., Maraoui, M., Zrigui, M.: Deep learning based semantic approach for Arabic textual documents recommendation. In: 2021 International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–6 (2021)
AlKhateeb, J.H., Ren, J., Jiang, J., Al-Muhtaseb, H.: Offline handwritten Arabic cursive text recognition using hidden Markov models and re-ranking. Pattern Recogn. Lett. 32, 1081–1088 (2011)
AlKhateeb, J.H., Pauplin, O., Ren, J., Jiang, J.: Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowl.-Based Syst. 24, 680–688 (2011)
Haffar, N., Hkiri, E., Zrigui, M.: Enrichment of Arabic TimeML corpus. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 655–667. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_51
Sghaier, M.A., Zrigui, M.: Rule-based machine translation from Tunisian dialect to modern standard Arabic. KES Procedia Comput. Sci. 176, 310–319 (2020)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, 781.MIT Press. http://www.deeplearningbook.org (2016)
Meddeb, O., Maraoui, M., Zrigui, M.: Arabic text documents recommendation using joint deep representations learning. KES Procedia Comput. Sci. 192, 812–821 (2021)
Mansouri, S., Charhad, M., Zrigui, M.: A heuristic approach to detect and localize text on Arabic news video. Computación y Sistemas 22(1) (2018)
Mansouri, S., Charhad, M., Zrigui, M.: Arabic text detection in news video based on line segment detector. Res. Comput. Sci. 132, 97–106 (2017)
Mansouri, S., Lhioui, C., Charhad, M., Zrigui, M.: Text-to-concept: a semantic indexing framework for arabic news videos. In: Gelbukh, A. (ed.) CICLing 2017. LNCS, vol. 10762, pp. 575–584. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77116-8_43
Mansouri, S., Zrigui, S., Zrigui, M., Berchech, D.: Text detection in Arabic news video based on MSER and RetinaNet. In: 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA), pp. 1–7 (2021)
Manita, S., Mansouri, S., Zrigui, M., Berchech, S.: Arabic text detection in news video using RetinaNet. KES Procedia Comput. Sci. 192, 796–803 (2021)
El-Sawy, A., Loey, M., El-Bakry, H.: Arabic handwritten characters recognition using convolutional neural network. WSEAS Trans. Comput. Res. 5, 11–19 (2017)
Maalej, R., Kherallah, M.: Convolutional neural network and BLSTM for offline Arabic handwriting recognition. In: International Arab Conference on Information Technology (ACIT), Werdanye, Lebanon, pp. 1–6 (2018)
Altwaijry, N., Al-Turaiki, I.: Arabic handwriting recognition system using convolutional neural network. Neural Comput. Appl. 33(7), 2249–2261 (2020). https://doi.org/10.1007/s00521-020-05070-8
https://towardsdatascience.com. Accessed 24 May 2021
Buduma, N.: Fundamentals of Deep Learning Designing Next-Generation Machine Intelligence Algorithms. O’Reilly Media, Inc. (2017)
https://github.com/christianversloot/machine-learning-articles/. Accessed 24 May 2021
Wu, J.: Introduction to Convolutional Neural Networks. National Key Lab for Novel Software Technology Nanjing University, China (2017)
Li, M., Zhang, T., Chen, Y., Smola, A.J.: Efficient mini-batch training for stochastic optimization. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 14, New York, NY, USA, pp. 661–670 (2014)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bouchriha, L., Zrigui, A., Mansouri, S., Berchech, S., Omrani, S. (2022). Arabic Handwritten Character Recognition Based on Convolution Neural Networks. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-16210-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16209-1
Online ISBN: 978-3-031-16210-7
eBook Packages: Computer ScienceComputer Science (R0)