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Open AccessArticle

Arabic Cursive Text Recognition from Natural Scene Images

Malaysia-Japan International Institute of Technology (M-JIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
Department of Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs (NGHA), Riyadh 11481, Saudi Arabia
GPGC No. 1, Higher Education Department, Abbottabad 22010, Pakistan
Department of Information Technology, University of Technology Sydney, Sydney 2007, Australia
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(2), 236;
Received: 29 November 2018 / Revised: 26 December 2018 / Accepted: 31 December 2018 / Published: 10 January 2019
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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. View Full-Text
Keywords: scene text recognition; Arabic cursive scripts; supervised learning; natural scene images; text recognition scene text recognition; Arabic cursive scripts; supervised learning; natural scene images; text recognition
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MDPI and ACS Style

Ahmed, S.B.; Naz, S.; Razzak, M.I.; Yusof, R. Arabic Cursive Text Recognition from Natural Scene Images. Appl. Sci. 2019, 9, 236.

AMA Style

Ahmed SB, Naz S, Razzak MI, Yusof R. Arabic Cursive Text Recognition from Natural Scene Images. Applied Sciences. 2019; 9(2):236.

Chicago/Turabian Style

Ahmed, Saad B.; Naz, Saeeda; Razzak, Muhammad I.; Yusof, Rubiyah. 2019. "Arabic Cursive Text Recognition from Natural Scene Images" Appl. Sci. 9, no. 2: 236.

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