Hicham Lamtougui^1,*, Hicham El Moubtahij², Hassan Fouadi¹, Khalid Satori¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2871-2888, 2023, DOI:10.32604/cmc.2023.032550 - 31 October 2022

Abstract In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term More >

Sofiene Haboubi^1,*, Tawfik Guesmi², Badr M Alshammari², Khalid Alqunun², Ahmed S Alshammari², Haitham Alsaif², Hamid Amiri¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5385-5397, 2022, DOI:10.32604/cmc.2022.029198 - 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 More >