A new method for detection and prediction of occluded text in natural scene images

Mittal, A; Shivakumara, P; Pal, U; Lu, T; Blumenstein, M

A new method for detection and prediction of occluded text in natural scene images

Mittal, A Shivakumara, P Pal, U Lu, T Blumenstein, M

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Signal Processing: Image Communication, 2022, 100, pp. 116512
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	A new method for detection and prediction.pdf		5.35 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Mittal, A
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	2023-03-09T23:31:19Z
dc.date.available	2023-03-09T23:31:19Z
dc.date.issued	2022-01-01
dc.identifier.citation	Signal Processing: Image Communication, 2022, 100, pp. 116512
dc.identifier.issn	0923-5965
dc.identifier.uri	http://hdl.handle.net/10453/166925
dc.description.abstract	Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Signal Processing: Image Communication
dc.relation.isbasedon	10.1016/j.image.2021.116512
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A new method for detection and prediction of occluded text in natural scene images
dc.type	Journal Article
utslib.citation.volume	100
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
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-09T23:31:17Z
pubs.publication-status	Published
pubs.volume	100

Abstract:

Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/166925