A Novel Adaptive Deskewing Algorithm for Document Images.

Bao, W; Yang, C; Wen, S; Zeng, M; Guo, J; Zhong, J; Xu, X

A Novel Adaptive Deskewing Algorithm for Document Images.

Bao, W Yang, C Wen, S

Zeng, M Guo, J Zhong, J Xu, X

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (20), pp. 7944
Issue Date:: 2022-10-18

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Field	Value	Language
dc.contributor.author	Bao, W
dc.contributor.author	Yang, C
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Zeng, M
dc.contributor.author	Guo, J
dc.contributor.author	Zhong, J
dc.contributor.author	Xu, X
dc.date.accessioned	2023-02-28T03:26:16Z
dc.date.available	2022-10-11
dc.date.available	2023-02-28T03:26:16Z
dc.date.issued	2022-10-18
dc.identifier.citation	Sensors (Basel), 2022, 22, (20), pp. 7944
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/166514
dc.description.abstract	Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image's layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC'2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22207944
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Cluster Analysis
dc.title	A Novel Adaptive Deskewing Algorithm for Document Images.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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	*
dc.date.updated	2023-02-28T03:26:10Z
pubs.issue	20
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	20

Abstract:

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image's layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC'2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.

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http://hdl.handle.net/10453/166514