A Document Image Dataset for Quality Assessment

Li, Z; Yang, C; Shen, Q; Wen, S

A Document Image Dataset for Quality Assessment

Li, Z Yang, C Shen, Q Wen, S

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Publisher:: Institute of Physics (IoP)
Publication Type:: Journal Article
Citation:: Journal of Physics: Conference Series, 2021, 1828, (1), pp. 1-9
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Li, Z
dc.contributor.author	Yang, C
dc.contributor.author	Shen, Q
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-02-01T08:16:02Z
dc.date.available	2024-02-01T08:16:02Z
dc.date.issued	2021-02-01
dc.identifier.citation	Journal of Physics: Conference Series, 2021, 1828, (1), pp. 1-9
dc.identifier.issn	1742-6588
dc.identifier.issn	1742-6596
dc.identifier.uri	http://hdl.handle.net/10453/175255
dc.description.abstract	Mobile device plays an very important role in capturing document image. However, the quality of the captured image is influenced by many factors, such as device quality and shooting conditions. In this context, it is necessary to automatically assess the quality of captured document image. Although there has a lot of work in the filed of image quality assessment (IQA), insufficient attention has been paid to the establishment of document images dataset. Thus, we propose a large dataset of document images containing 19,943 images which are collected by mobile devices. During the process of image acquisition, many factors such as light intensity, distortion type, document material are considered. After capturing images, multiple volunteers participated in the evaluation and collection of Mean Opinion Score (MOS) of the document images. We use two no-reference image quality assessment algorithms to test the proposed dataset. The experimental results show the validity of our dataset and the reliability of MOS. The proposed dataset can be used in the field of image quality assessment and Optical Character Recognition.
dc.language	en
dc.publisher	Institute of Physics (IoP)
dc.relation.ispartof	Journal of Physics: Conference Series
dc.relation.isbasedon	10.1088/1742-6596/1828/1/012033
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics, 0204 Condensed Matter Physics, 0299 Other Physical Sciences
dc.subject.classification	51 Physical sciences
dc.title	A Document Image Dataset for Quality Assessment
dc.type	Journal Article
utslib.citation.volume	1828
utslib.for	0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics
utslib.for	0204 Condensed Matter Physics
utslib.for	0299 Other Physical Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2024-02-01T08:16:01Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	1828
utslib.citation.issue	1

Abstract:

Mobile device plays an very important role in capturing document image. However, the quality of the captured image is influenced by many factors, such as device quality and shooting conditions. In this context, it is necessary to automatically assess the quality of captured document image. Although there has a lot of work in the filed of image quality assessment (IQA), insufficient attention has been paid to the establishment of document images dataset. Thus, we propose a large dataset of document images containing 19,943 images which are collected by mobile devices. During the process of image acquisition, many factors such as light intensity, distortion type, document material are considered. After capturing images, multiple volunteers participated in the evaluation and collection of Mean Opinion Score (MOS) of the document images. We use two no-reference image quality assessment algorithms to test the proposed dataset. The experimental results show the validity of our dataset and the reliability of MOS. The proposed dataset can be used in the field of image quality assessment and Optical Character Recognition.

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http://hdl.handle.net/10453/175255