A new augmentation-based method for text detection in night and day license plate images

Chowdhury, PN; Shivakumara, P; Pal, U; Lu, T; Blumenstein, M

A new augmentation-based method for text detection in night and day license plate images

Chowdhury, PN Shivakumara, P Pal, U Lu, T Blumenstein, M

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2020, 79, (43-44), pp. 33303-33330
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Chowdhury, PN
dc.contributor.author	Shivakumara, P
dc.contributor.author	Pal, U
dc.contributor.author	Lu, T
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2021-05-24T07:03:49Z
dc.date.available	2021-05-24T07:03:49Z
dc.date.issued	2020-11-01
dc.identifier.citation	Multimedia Tools and Applications, 2020, 79, (43-44), pp. 33303-33330
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/149157
dc.description.abstract	Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-020-09681-0
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Software Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A new augmentation-based method for text detection in night and day license plate images
dc.type	Journal Article
utslib.citation.volume	79
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0801 Artificial Intelligence and Image Processing
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
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-09-01T00:00:00+1000Z
dc.date.updated	2021-05-24T07:03:43Z
pubs.issue	43-44
pubs.publication-status	Published
pubs.volume	79
utslib.citation.issue	43-44

Abstract:

Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed.

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http://hdl.handle.net/10453/149157