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Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances

Ahmed, Rami; Dashtipour, Kia; Gogate, Mandar; Raza, Ali; Zhang, Rui; Huang, Kaizhu; Hawalah, Ahmad; Adeel, Ahsan; Hussain, Amir


Rami Ahmed

Ali Raza

Rui Zhang

Kaizhu Huang

Ahmad Hawalah

Ahsan Adeel


In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, application forms processing, postal address processing, to text-to-speech conversion. However, most research efforts are devoted to English-language only. This work focuses on developing Offline Arabic Handwriting Recognition (OAHR). The OAHR is a very challenging task due to some unique characteristics of the Arabic script such as cursive nature, ligatures, overlapping, and diacritical marks. In the recent literature, several effective Deep Learning (DL) approaches have been proposed to develop efficient AHWR systems. In this paper, we commission a survey on emerging AHWR technologies with some insight on OAHR background, challenges, opportunities, and future research trends.

Presentation Conference Type Conference Paper (Published)
Conference Name 10th International Conference, BICS 2019
Start Date Jul 13, 2019
End Date Jul 14, 2019
Online Publication Date Feb 1, 2020
Publication Date 2020
Deposit Date Apr 26, 2022
Publisher Springer
Peer Reviewed Peer Reviewed
Pages 457-468
Series Title Lecture Notes in Computer Science
Series Number 11691
Series ISSN 0302-9743
Book Title Advances in Brain Inspired Cognitive Systems: 10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings
ISBN 978-3-030-39430-1
Keywords Offline Arabic Handwritten Recognition, Offline Arabic database, Deep Learning
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