Offline arabic handwriting recognition using deep machine learning: A review of recent advances

Citation

Ahmed R, Dashtipour K, Gogate M, Raza A, Zhang R, Huang K, Hawalah A, Adeel A & Hussain A (2020) Offline arabic handwriting recognition using deep machine learning: A review of recent advances. In: Ren J, Hussain A, Zhao H, Huang K, Zheng J, Cai J, Chen R & Xiao Y (eds.) Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science, 11691. BICS 2019: 10th International Conference on Brain Inspired Cognitive Systems, Guangzhou, China, 13.07.2019-14.07.2019. Cham, Switzerland: Springer, pp. 457-468. https://doi.org/10.1007/978-3-030-39431-8_44

Abstract
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.

Keywords
Offline Arabic Handwritten Recognition; Offline Arabic database; Deep Learning