In recent times, many research projects and experiments target machines that automatically recognize handwritten characters, but most of them are done in Latin. Recognizing handwritten Arabic characters is a complicated process compared to English and other languages as a nature of Arabic words. In the past few years, deep learning approaches have been increasingly used in the field of Arabic recognition. This paper aims to categorize, analyze and presents a comprehensive survey in Arabic handwritten recognition literature, focusing on state-of-the-art methods for deep learning in feature extraction. The paper focuses on offline text recognition, with a detailed discussion of the systematic analysis of the literature. Additionally, the paper is critically analyzing the current literature and identifying the problem areas and challenges faced by the previous studies. After investigating the studies, a new classification of the literature is proposed. Besides, an analysis is performed based on the findings, and several issues and challenges related to the recognition of Arabic scripts are discussed.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
“Babbel Magazine.” https://www.babbel.com/en/magazine/ (accessed Mar. 09, 2021).
N. Altwaijry and I. Al-Turaiki, “Arabic handwriting recognition system using convolutional neural network,” Neural Comput. Appl., pp. 1–13, Jun. 2020, doi: https://doi.org/10.1007/s00521-020-05070-8.
Weldegebriel, H.T.; Liu, H.; Haq, A.U.; Bugingo, E.; Zhang, D.: A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters. IEEE Access 8, 17804–17818 (2020). https://doi.org/10.1109/ACCESS.2019.2960161
Albahli, Saleh, Marriam Nawaz, Ali Javed, and Aun Irtaza. "An improved faster-RCNN model for handwritten character recognition." Arabian Journal for Science and Engineering (2021): 1–15.
Boufenar, C.; Kerboua, A.; Batouche, M.: Investigation on deep learning for off-line handwritten Arabic character recognition. Cogn. Syst. Res. 50, 180–195 (Aug. 2018). https://doi.org/10.1016/j.cogsys.2017.11.002
de Sousa, I.P.: Convolutional ensembles for Arabic Handwritten Character and Digit Recognition. PeerJ Comput. Sci. 2018(10), e167 (Oct. 2018). https://doi.org/10.7717/peerj-cs.167
R. Ahmed et al., “Offline arabic handwriting recognition using deep machine learning: A review of recent advances,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Jul. 2020, vol. 11691 LNAI, pp. 457–468, doi: https://doi.org/10.1007/978-3-030-39431-8_44.
H. Q. Ghadhban, M. Othman, N. A. Samsudin, M. N. Bin Ismail, and M. R. Hammoodi, “Survey of Offline Arabic Handwriting Word Recognition,” in Advances in Intelligent Systems and Computing, Jan. 2020, vol. 978 AISC, pp. 358–372, doi: https://doi.org/10.1007/978-3-030-36056-6_34.
Ali, A.A.A.; Suresha, M.; Ahmed, H.A.M.: A Survey on Arabic Handwritten Character Recognition. SN Comput. Sci. 1(3), 1–10 (May 2020). https://doi.org/10.1007/s42979-020-00168-1
M. Elleuch, N. Tagougui, and M. Kherallah, “Arabic handwritten characters recognition using Deep Belief Neural Networks,” Dec. 2015, doi: https://doi.org/10.1109/SSD.2015.7348121.
Elleuch, M.; Tagougui, N.; Kherallah, M.: Towards unsupervised learning for arabic handwritten recognition using deep architectures. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9489, 363–372 (2015). https://doi.org/10.1007/978-3-319-26532-2_40
Kherallah, M.; Elleuch, M.; Tagougui, N.: A novel architecture of CNN based on SVM classifier for recognising Arabic handwritten script. Int. J. Intell. Syst. Technol. Appl. 15(4), 323 (2016). https://doi.org/10.1504/ijista.2016.10000779
Elleuch, M.; Maalej, R.; Kherallah, M.: A New design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Procedia Computer Science 80, 1712–1723 (Jan. 2016). https://doi.org/10.1016/j.procs.2016.05.512
M. Elleuch, R. Mokni, and M. Kherallah, “Offline Arabic Handwritten recognition system with dropout applied in Deep networks based-SVMs,” in Proceedings of the International Joint Conference on Neural Networks, Oct. 2016, vol. 2016-October, pp. 3241–3248, doi: https://doi.org/10.1109/IJCNN.2016.7727613.
Elleuch, M.; Kherallah, M.: An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines. Int. J. Multimed. Data Eng. Manag. 7(2), 1–20 (May 2016). https://doi.org/10.4018/ijmdem.2016040101
A. Poznanski and L. Wolf, “CNN-N-Gram for HandwritingWord Recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2016, vol. 2016-December, pp. 2305–2314, doi: https://doi.org/10.1109/CVPR.2016.253.
Maalej, R.; Tagougui, N.; Kherallah, M.: Recognition of handwritten arabic words with dropout applied in MDLSTM. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9730, 746–752 (2016). https://doi.org/10.1007/978-3-319-41501-7_83
R. Maalej and M. Kherallah, “Improving MDLSTM for offline Arabic handwriting recognition using dropout at different positions,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9887 LNCS, pp. 431–438, doi: https://doi.org/10.1007/978-3-319-44781-0_51.
El-Sawy, A.; Loey, M.; El-Bakry, H.: Arabic Handwritten Characters Recognition using Convolutional Neural Network. WSEAS Trans. Comput. Res. 5(1), 11–19 (2017)
K. Younis, “ARABIC HANDWRITTEN CHARACTER RECOGNITION BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS,” 2018.
Mudhsh, M.A.; Almodfer, R.: Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network. Information 8(3), 105 (Aug. 2017). https://doi.org/10.3390/info8030105
B. Mohamed Anas Hussein Al-Jubouri, “Offline Arabic Handwritten Isolated Character Recognition System Using Support vector Machine and Neural Network ,” 2017
Elleuch, M.; Tagougui, N.; Kherallah, M.: Optimization of DBN using Regularization Methods Applied for Recognizing Arabic Handwritten Script. Procedia Computer Science 108, 2292–2297 (Jan. 2017). https://doi.org/10.1016/j.procs.2017.05.070
R. Almodfer, S. Xiong, M. Mudhsh, and P. Duan, “Multi-column deep neural network for offline arabic handwriting recognition,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10614 LNCS, pp. 260–267, doi: https://doi.org/10.1007/978-3-319-68612-7_30.
R. Almodfer, S. Xiong, M. Mudhsh, and P. Duan, “Enhancing alexnet for Arabic handwritten words recognition using incremental dropout,” in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, Jun. 2018, vol. 2017-November, pp. 663–669, doi: https://doi.org/10.1109/ICTAI.2017.00106.
M. Elleuch, A. M. Alimi, and M. Kherallah, “Enhancement of Deep Architecture using Dropout/ DropConnect Techniques Applied for AHR System,” in Proceedings of the International Joint Conference on Neural Networks, Oct. 2018, vol. 2018-July, doi: https://doi.org/10.1109/IJCNN.2018.8489245.
R. Maalej and M. Kherallah, “Convolutional Neural Network and BLSTM for Offline Arabic Handwriting Recognition,” Mar. 2019, doi: https://doi.org/10.1109/ACIT.2018.8672667.
M. Amrouch, M. Rabi, and Y. Es-Saady, “Convolutional feature learning and CNN based HMM for Arabic handwriting recognition,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Jul. 2018, vol. 10884 LNCS, pp. 265–274, doi: https://doi.org/10.1007/978-3-319-94211-7_29.
H. M. Najadat, A. A. Alshboul, and A. F. Alabed, “Arabic Handwritten Characters Recognition using Convolutional Neural Network,” in 2019 10th International Conference on Information and Communication Systems, ICICS 2019, Jun. 2019, pp. 147–151, doi: https://doi.org/10.1109/IACS.2019.8809122.
O. A. Almansari and N. N. W. N. Hashim, “Recognition of Isolated Handwritten Arabic Characters,” Oct. 2019, doi: https://doi.org/10.1109/ICOM47790.2019.8952035.
Elleuch, M.; Kherallah, M.: Boosting of Deep Convolutional Architectures for Arabic Handwriting Recognition. Int. J. Multimed. Data Eng. Manag. 10(4), 26–45 (Dec. 2019). https://doi.org/10.4018/ijmdem.2019100102
Ali, A.A.A.; Suresha, M.: A novel features and classifiers fusion technique for recognition of Arabic handwritten character script. SN Appl. Sci. 1(10), 1–13 (Oct. 2019). https://doi.org/10.1007/s42452-019-1294-6
M. El-Melegy, A. Abdelbaset, A. Abdel-Hakim, and G. El-Sayed, “Recognition of Arabic Handwritten Literal Amounts Using Deep Convolutional Neural Networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11868 LNCS, pp. 169–176, doi: https://doi.org/10.1007/978-3-030-31321-0_15.
R. Maalej and M. Kherallah, “Maxout into MDLSTM for offline arabic handwriting recognition,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Dec. 2019, vol. 11955 LNCS, pp. 534–545, doi: https://doi.org/10.1007/978-3-030-36718-3_45.
Khémiri, A.; Echi, A.K.; Elloumi, M.: Bayesian Versus Convolutional Networks for Arabic Handwriting Recognition. Arab. J. Sci. Eng. 44(11), 9301–9319 (Nov. 2019). https://doi.org/10.1007/s13369-019-03939-y
M. Awni, M. I. Khalil, and H. M. Abbas, “Deep-learning ensemble for offline arabic handwritten words recognition,” in Proceedings - ICCES 2019: 2019 14th International Conference on Computer Engineering and Systems, Dec. 2019, pp. 40–45, doi: https://doi.org/10.1109/ICCES48960.2019.9068184.
H. Alyahya, M. M. Ben Ismail, and A. Al-Salman, “Deep ensemble neural networks for recognizing isolated Arabic handwritten characters,” Accent. Trans. Image Process. Comput. Vis., vol. 6, no. 21, pp. 68–79, Nov. 2020, doi: https://doi.org/10.19101/tipcv.2020.618051.
H. M. Balaha, H. A. Ali, and M. Badawy, “Automatic recognition of handwritten Arabic characters: a comprehensive review,” Neural Computing and Applications. Springer, pp. 1–24, Jul. 17, 2020, doi: https://doi.org/10.1007/s00521-020-05137-6.
M. Shams, A. A., and W. Z., “Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 8, pp. 144–149, 2020, doi: https://doi.org/10.14569/IJACSA.2020.0110819.
Al-Jourishi, A.A.; Omari, M.: Handwritten Arabic characters recognition using a hybrid two-stage classifier. Int. J. Adv. Comput. Sci. Appl. 11(6), 143–148 (2020). https://doi.org/10.14569/IJACSA.2020.0110619
Ghanim, T.M.; Khalil, M.I.; Abbas, H.M.: Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition. IEEE Access 8, 95465–95482 (2020). https://doi.org/10.1109/ACCESS.2020.2994290
Mustafa, M.E.; Elbashir, M.K.: A deep learning approach for handwritten Arabic names recognition. Int. J. Adv. Comput. Sci. Appl. 11(1), 678–682 (2020). https://doi.org/10.14569/ijacsa.2020.0110183
R. Ahmad, S. Naz, M. Afzal, M. Liwicki, and A. Dengel, “A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT,” Int. Arab J. Inf. Technol., vol. 17, no. 3, 2020, doi: https://doi.org/10.34028/iajit/17/3/3.
Eltay, M.; Zidouri, A.; Ahmad, I.: Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts. IEEE Access 8, 89882–89898 (2020). https://doi.org/10.1109/ACCESS.2020.2994248
I. Due Trier, A. K. Jain, and T. Taxt, “FEATURE EXTRACTION METHODS FOR CHARACTER RECOGNITION | A SURVEY,” Pattern Recognit., vol. 29, no. 4, pp. 641–662, 1996.
Hicham, E.M.; Akram, H.; Khalid, S.: Using features of local densities, statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition. J. Electr. Syst. Inf. Technol. 4(3), 387–396 (Dec. 2017). https://doi.org/10.1016/j.jesit.2016.07.005
R. Hussain, A. Raza, I. Siddiqi, K. Khurshid, and C. Djeddi, “A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation,” Eurasip Journal on Image and Video Processing, vol. 2015, no. 1. Springer International Publishing, pp. 1–24, Dec. 01, 2015, doi: https://doi.org/10.1186/s13640-015-0102-5.
M. Torki, M. E. Hussein, A. Elsallamy, M. Fayyaz, and S. Yaser, “WINDOW-BASED DESCRIPTORS FOR ARABIC HANDWRITTEN ALPHABET RECOGNITION: A COMPARATIVE STUDY ON A NOVEL DATASET.”
A. El Sawy, H. El-Bakry, and M. Loey, “Arabic Handwritten Characters Dataset (AHCD).” .
“HACDB: Handwritten Arabic characters database for automatic character recognition - IEEE Conference Publication.” https://ieeexplore-ieee-org.sdl.idm.oclc.org/document/6623974 (accessed Jan. 17, 2021).
H. M. Balaha, H. A. Ali, M. Saraya, and M. Badawy, “A new Arabic handwritten character recognition deep learning system (AHCR-DLS),” Neural Comput. Appl., pp. 1–43, Oct. 2020, doi: https://doi.org/10.1007/s00521-020-05397-2.
“(18) (PDF) IFN/ENIT-database of handwritten Arabic words.” https://www.researchgate.net/publication/228904501_IFNENIT-database_of_handwritten_Arabic_words (accessed Jan. 13, 2021).
S. Al-Ma’adeed, D. Elliman, and C. Higgins, “A Data Base for Arabic Handwritten Text Recognition Research,” 2004.
M. Musa, “Towards building competent dataset of Arabic recognition,” Int. J. Eng. Adv. Res. Technol., vol. 2, no. 2, Feb. 2016, Accessed: Jan. 24, 2021. [Online]. Available: www.sustech.edu.
Mahmoud, S.A., et al.: KHATT: An open Arabic offline handwritten text database. Pattern Recognit. 47(3), 1096–1112 (2014). https://doi.org/10.1016/j.patcog.2013.08.009
S. A. Mahmoud et al., “KHATT: An open Arabic offline handwritten text database,” 2013, doi: https://doi.org/10.1016/j.patcog.2013.08.009.
K. Simonyan and A. Zisserman, “VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION,” 2015. Accessed: Nov. 15, 2020. [Online]. Available: http://www.robots.ox.ac.uk/.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2016, vol. 2016-December, pp. 770–778, doi: https://doi.org/10.1109/CVPR.2016.90.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks.” Accessed: Nov. 19, 2020. [Online]. Available: http://code.google.com/p/cuda-convnet/.
S. Liu and W. Deng, “Very deep convolutional neural network based image classification using small training sample size,” in Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, Jun. 2016, pp. 730–734, doi: https://doi.org/10.1109/ACPR.2015.7486599.
C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Oct. 2015, vol. 07–12-June-2015, pp. 1–9, doi: https://doi.org/10.1109/CVPR.2015.7298594.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Nov. 2017, vol. 2017-January, pp. 5987–5995, doi: https://doi.org/10.1109/CVPR.2017.634.
Y. Zhu and S. Newsam, “DenseNet for dense flow,” in Proceedings - International Conference on Image Processing, ICIP, Feb. 2018, vol. 2017-September, pp. 790–794, doi: https://doi.org/10.1109/ICIP.2017.8296389.
Cireşan, D.; Meier, U.; Masci, J.; Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (Aug. 2012). https://doi.org/10.1016/j.neunet.2012.02.023
N. Srivastava, G. Hinton, A. Krizhevsky, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” 2014. Accessed: Mar. 02, 2021. [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.html.
A. Granet et al., “Trans-fer Learning for Handwriting Recognition on Historical Documents,” Jan. 2018. Accessed: Jan. 27, 2021. [Online]. Available: https://hal.archives-ouvertes.fr/hal-01681126.
N. Srivastava, G. Hinton, A. Krizhevsky, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” 2014. Accessed: Apr. 04, 2021. [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.html.
S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” PMLR, Jun. 2015. Accessed: Apr. 04, 2021. [Online]. Available: http://proceedings.mlr.press/v37/ioffe15.html.
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, “Maxout Networks,” in Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013, pp. 1319–1327.
A. Krogh· and J. A. Hertz, “A Simple Weight Decay Can Improve Generalization,” in Proceedings of the 4th International Conference on Neural Information Processing Systems, 1991, pp. 950–957.
L. Wan, M. Zeiler, S. Zhang, Y. Lecun, and R. Fergus, “Regularization of Neural Networks using DropConnect,” PMLR, May 2013. Accessed: Apr. 04, 2021. [Online]. Available: http://proceedings.mlr.press/v28/wan13.html.
Alrobah N, Albahli S. A Hybrid Deep Model for Recognizing Arabic Handwritten Characters. IEEE Access. 2021 Jun 8.
Alsaffar, Ahmed & Awang, Suryanti & AL-Saiagh, Wafaa & Tiun, Sabrina & Al-Khaleefa, Ahmed Salih. (2018). Deep Learning Algorithms for Arabic Handwriting Recognition: A Review. International Journal of Engineering & Technology. 7. 344. https://doi.org/10.14419/ijet.v7i3.20.19271.
Musa, Mohamed Elhafiz. (2011). Arabic handwritten datasets for pattern recognition and machine learning. 1–3. https://doi.org/10.1109/ICAICT.2011.6110959.
Noubigh, Zouhaira & Anis, Mezghani. (2021). Contribution on Arabic Handwriting Recognition Using Deep Neural Network. https://doi.org/10.1007/978-3-030-49336-3_13
Alkhawaldeh, R.: Arabic (Indian) digit handwritten recognition using recurrent transfer deep architecture. Soft. Comput. 1–11,(2021). https://doi.org/10.1007/s00500-020-05368-8
About this article
Cite this article
Alrobah, N., Albahli, S. Arabic Handwritten Recognition Using Deep Learning: A Survey. Arab J Sci Eng 47, 9943–9963 (2022). https://doi.org/10.1007/s13369-021-06363-3