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Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Sofiene Haboubi1,*, Tawfik Guesmi2, Badr M Alshammari2, Khalid Alqunun2, Ahmed S Alshammari2, Haitham Alsaif2, Hamid Amiri1

1 Signals Images and Information Technologies Lab. University of Tunis El Manar, National Engineering School of Tunis, BP 37 Le Belvedere, Tunis, 1002, Tunisia
2 Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il, 2440, Saudi Arabia

* Corresponding Author: Sofiene Haboubi. Email: email

Computers, Materials & Continua 2022, 73(3), 5385-5397. https://doi.org/10.32604/cmc.2022.029198

Abstract

Handwriting recognition is a challenge that interests many researchers around the world. As an exception, handwritten Arabic script has many objectives that remain to be overcome, given its complex form, their number of forms which exceeds 100 and its cursive nature. Over the past few years, good results have been obtained, but with a high cost of memory and execution time. In this paper we propose to improve the capacity of bidirectional gated recurrent unit (BGRU) to recognize Arabic text. The advantages of using BGRUs is the execution time compared to other methods that can have a high success rate but expensive in terms of time and memory. To test the recognition capacity of BGRU, the proposed architecture is composed by 6 convolutional neural network (CNN) blocks for feature extraction and 1 BGRU + 2 dense layers for learning and test. The experiment is carried out on the entire database of institut für nachrichtentechnik/ecole nationale d’ingénieurs de Tunis (IFN/ENIT) without any preprocessing or data selection. The obtained results show the ability of BGRUs to recognize handwritten Arabic script.

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APA Style
Haboubi, S., Guesmi, T., Alshammari, B.M., Alqunun, K., Alshammari, A.S. et al. (2022). Improving CNN-BGRU hybrid network for arabic handwritten text recognition. Computers, Materials & Continua, 73(3), 5385-5397. https://doi.org/10.32604/cmc.2022.029198
Vancouver Style
Haboubi S, Guesmi T, Alshammari BM, Alqunun K, Alshammari AS, Alsaif H, et al. Improving CNN-BGRU hybrid network for arabic handwritten text recognition. Comput Mater Contin. 2022;73(3):5385-5397 https://doi.org/10.32604/cmc.2022.029198
IEEE Style
S. Haboubi et al., "Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition," Comput. Mater. Contin., vol. 73, no. 3, pp. 5385-5397. 2022. https://doi.org/10.32604/cmc.2022.029198



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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