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

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

doi:10.1007/978-3-030-39431-8_44

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

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

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.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
Editors	Jinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
Publisher	Springer
Pages	457-468
Number of pages	12
ISBN (Print)	9783030394301
DOIs	https://doi.org/10.1007/978-3-030-39431-8_44
State	Published - 2020
Externally published	Yes
Event	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China Duration: 13 Jul 2019 → 14 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11691 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
Country/Territory	China
City	Guangzhou
Period	13/07/19 → 14/07/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Deep Learning
Offline Arabic Handwritten Recognition
Offline Arabic database

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-39431-8_44

Cite this

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 J. Ren, A. Hussain, H. Zhao, J. Cai, R. Chen, Y. Xiao, K. Huang, & J. Zheng (Eds.), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings (pp. 457-468). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11691 LNAI). Springer. https://doi.org/10.1007/978-3-030-39431-8_44

Ahmed, Rami ; Dashtipour, Kia ; Gogate, Mandar et al. / Offline arabic handwriting recognition using deep machine learning : A review of recent advances. Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. editor / Jinchang Ren ; Amir Hussain ; Huimin Zhao ; Jun Cai ; Rongjun Chen ; Yinyin Xiao ; Kaizhu Huang ; Jiangbin Zheng. Springer, 2020. pp. 457-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{124305df2d0e46aa9fad2e8ef9fac118,

title = "Offline arabic handwriting recognition using deep machine learning: A review of recent advances",

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 = "Deep Learning, Offline Arabic Handwritten Recognition, Offline Arabic database",

author = "Rami Ahmed and Kia Dashtipour and Mandar Gogate and Ali Raza and Rui Zhang and Kaizhu Huang and Ahmad Hawalah and Ahsan Adeel and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 ; Conference date: 13-07-2019 Through 14-07-2019",

year = "2020",

doi = "10.1007/978-3-030-39431-8\_44",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Jinchang Ren and Amir Hussain and Huimin Zhao and Jun Cai and Rongjun Chen and Yinyin Xiao and Kaizhu Huang and Jiangbin Zheng",

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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 J Ren, A Hussain, H Zhao, J Cai, R Chen, Y Xiao, K Huang & J Zheng (eds), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11691 LNAI, Springer, pp. 457-468, 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019, Guangzhou, China, 13/07/19. https://doi.org/10.1007/978-3-030-39431-8_44

Offline arabic handwriting recognition using deep machine learning: A review of recent advances. / Ahmed, Rami; Dashtipour, Kia; Gogate, Mandar et al.
Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. ed. / Jinchang Ren; Amir Hussain; Huimin Zhao; Jun Cai; Rongjun Chen; Yinyin Xiao; Kaizhu Huang; Jiangbin Zheng. Springer, 2020. p. 457-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11691 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - https://www.scopus.com/pages/publications/85080912739

U2 - 10.1007/978-3-030-39431-8_44

DO - 10.1007/978-3-030-39431-8_44

M3 - Conference contribution

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 -

Ahmed R, Dashtipour K, Gogate M, Raza A, Zhang R, Huang K et al. Offline arabic handwriting recognition using deep machine learning: A review of recent advances. In Ren J, Hussain A, Zhao H, Cai J, Chen R, Xiao Y, Huang K, Zheng J, editors, Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Springer. 2020. p. 457-468. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39431-8_44

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this