Openly accessible

Arabic cursive text recognition from natural scene images

Ahmed, Saad Bin, Naz, Saeeda, Razzak, Muhammad Imran and Yusof, Rubiyah 2019, Arabic cursive text recognition from natural scene images, Applied sciences, vol. 9, no. 2, doi: 10.3390/app9020236.

Attached Files
Name Description MIMEType Size Downloads

Title Arabic cursive text recognition from natural scene images
Author(s) Ahmed, Saad Bin
Naz, Saeeda
Razzak, Muhammad ImranORCID iD for Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600
Yusof, Rubiyah
Journal name Applied sciences
Volume number 9
Issue number 2
Total pages 27
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019
ISSN 2076-3417
2076-3417
Summary © 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.
Language eng
DOI 10.3390/app9020236
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2019, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30132543

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 7 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 68 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Tue, 26 Nov 2019, 08:35:41 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.