Open Access

ARTICLE

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Sofiene Haboubi^1,*, 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:

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

Received 27 February 2022; Accepted 06 May 2022; Issue published 28 July 2022

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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Keywords

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