TY  - EJOU
AU  - Haboubi, Sofiene 
AU  - Guesmi, Tawfik 
AU  - Alshammari, Badr M 
AU  - Alqunun, Khalid 
AU  - Alshammari, Ahmed S 
AU  - Alsaif, Haitham 
AU  - Amiri, Hamid 

TI  - Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 73
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Arabic handwritten script; handwritten text recognition; deep learning; IFN/ENIT; bidirectional GRU; neural network

DO  - 10.32604/cmc.2022.029198