Arabic Handwritten Recognition Using Deep Learning: A Survey

被引：0
作者：
Naseem Alrobah
Saleh Albahli
机构：
[1] Mustaqbal University,Department of Computer Engineering, College of Engineering and Computer Science
[2] Qassim University,Department of Information Technology, College of Computer
来源：
Arabian Journal for Science and Engineering | 2022年 / 47卷
关键词：
Arabic Handwritten Recognition (AHR); Deep learning; Arabic script; Feature extraction; Classification; Arabization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In recent times, many research projects and experiments target machines that automatically recognize handwritten characters, but most of them are done in Latin. Recognizing handwritten Arabic characters is a complicated process compared to English and other languages as a nature of Arabic words. In the past few years, deep learning approaches have been increasingly used in the field of Arabic recognition. This paper aims to categorize, analyze and presents a comprehensive survey in Arabic handwritten recognition literature, focusing on state-of-the-art methods for deep learning in feature extraction. The paper focuses on offline text recognition, with a detailed discussion of the systematic analysis of the literature. Additionally, the paper is critically analyzing the current literature and identifying the problem areas and challenges faced by the previous studies. After investigating the studies, a new classification of the literature is proposed. Besides, an analysis is performed based on the findings, and several issues and challenges related to the recognition of Arabic scripts are discussed.
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页码：9943 / 9963
页数：20
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  • [1] Weldegebriel HT(2020)A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters IEEE Access 8 17804-17818
  • [2] Liu H(2018)Investigation on deep learning for off-line handwritten Arabic character recognition Cogn. Syst. Res. 50 180-195
  • [3] Haq AU(2018)Convolutional ensembles for Arabic Handwritten Character and Digit Recognition PeerJ Comput. Sci. 2018 1-10
  • [4] Bugingo E(2020)A Survey on Arabic Handwritten Character Recognition SN Comput. Sci. 1 363-372
  • [5] Zhang D(2015)Towards unsupervised learning for arabic handwritten recognition using deep architectures Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9489 323-1723
  • [6] Boufenar C(2016)A novel architecture of CNN based on SVM classifier for recognising Arabic handwritten script Int. J. Intell. Syst. Technol. Appl. 15 1712-20
  • [7] Kerboua A(2016)A New design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition Procedia Computer Science 80 1-752
  • [8] Batouche M(2016)An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines Int. J. Multimed. Data Eng. Manag. 7 746-19
  • [9] de Sousa IP(2016)Recognition of handwritten arabic words with dropout applied in MDLSTM Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9730 11-2297
  • [10] Ali AAA(2017)Arabic Handwritten Characters Recognition using Convolutional Neural Network WSEAS Trans. Comput. Res. 5 105-45
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