TY - GEN
T1 - Offline arabic handwriting recognition using deep machine learning
T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
AU - Ahmed, Rami
AU - Dashtipour, Kia
AU - Gogate, Mandar
AU - Raza, Ali
AU - Zhang, Rui
AU - Huang, Kaizhu
AU - Hawalah, Ahmad
AU - Adeel, Ahsan
AU - Hussain, Amir
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Deep Learning
KW - Offline Arabic Handwritten Recognition
KW - Offline Arabic database
UR - http://www.scopus.com/inward/record.url?scp=85080912739&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39431-8_44
DO - 10.1007/978-3-030-39431-8_44
M3 - Conference Proceeding
AN - SCOPUS:85080912739
SN - 9783030394301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 457
EP - 468
BT - Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
A2 - Ren, Jinchang
A2 - Hussain, Amir
A2 - Zhao, Huimin
A2 - Cai, Jun
A2 - Chen, Rongjun
A2 - Xiao, Yinyin
A2 - Huang, Kaizhu
A2 - Zheng, Jiangbin
PB - Springer
Y2 - 13 July 2019 through 14 July 2019
ER -