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A novel dataset for English-Arabic scene text recognition (EASTR)-42K and its evaluation using invariant feature extraction on detected extremal regions

Ahmed, Saad Bin, Naz, Saeeda, Razzak, Muhammad Imran and Yusof, Rubiyah Bte 2019, A novel dataset for English-Arabic scene text recognition (EASTR)-42K and its evaluation using invariant feature extraction on detected extremal regions, IEEE access, vol. 7, pp. 19801-19820, doi: 10.1109/ACCESS.2019.2895876.

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Title A novel dataset for English-Arabic scene text recognition (EASTR)-42K and its evaluation using invariant feature extraction on detected extremal regions
Author(s) Ahmed, Saad Bin
Naz, Saeeda
Razzak, Muhammad ImranORCID iD for Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600
Yusof, Rubiyah Bte
Journal name IEEE access
Volume number 7
Start page 19801
End page 19820
Total pages 20
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
2169-3536
Summary © 2019 IEEE. The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination. English as a secondary language is extensively used in Gulf countries along with Arabic script. Therefore, this paper introduces English-Arabic scene text recognition 42K scene text image dataset. The dataset includes text images appeared in English and Arabic scripts while maintaining the prime focus on Arabic script. The dataset can be employed for the evaluation of text segmentation and recognition task. To provide an insight to other researchers, experiments have been carried out on the segmentation and classification of Arabic as well as English text and report error rates like 5.99% and 2.48%, respectively. This paper presents a novel technique by using adapted maximally stable extremal region (MSER) technique and extracts scale-invariant features from MSER detected region. To select discriminant and comprehensive features, the size of invariant features is restricted and considered those specific features which exist in the extremal region. The adapted MDLSTM network is presented to tackle the complexities of cursive scene text. The research on Arabic scene text is in its infancy, thus this paper presents benchmark work in the field of text analysis.
Language eng
DOI 10.1109/ACCESS.2019.2895876
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2019, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30132542

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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.