skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Arabic Handwritten Recognition Using Deep Learning: A Survey

Arabian journal for science and engineering (2011), 2022-08, Vol.47 (8), p.9943-9963 [Peer Reviewed Journal]

King Fahd University of Petroleum & Minerals 2021 ;King Fahd University of Petroleum & Minerals 2021. ;ISSN: 2193-567X ;ISSN: 1319-8025 ;EISSN: 2191-4281 ;DOI: 10.1007/s13369-021-06363-3

Full text available

Citations Cited by
  • Title:
    Arabic Handwritten Recognition Using Deep Learning: A Survey
  • Author: Alrobah, Naseem ; Albahli, Saleh
  • Subjects: Character recognition ; Deep learning ; Engineering ; Feature extraction ; Handwriting recognition ; Humanities and Social Sciences ; Machine learning ; multidisciplinary ; Research Article-Computer Engineering and Computer Science ; Research projects ; Science
  • Is Part Of: Arabian journal for science and engineering (2011), 2022-08, Vol.47 (8), p.9943-9963
  • Description: 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.
  • Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
  • Language: English
  • Identifier: ISSN: 2193-567X
    ISSN: 1319-8025
    EISSN: 2191-4281
    DOI: 10.1007/s13369-021-06363-3
  • Source: GFMER Free Medical Journals
    Alma/SFX Local Collection
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait