A system for licence plate recognition using a hierarchically combined classifier

Zheng, L; He, X; Wu, Q; Samali, B

A system for licence plate recognition using a hierarchically combined classifier

Zheng, L He, X

Wu, Q

Samali, B

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Publication Type:: Journal Article
Citation:: International Journal of Intelligent Systems Technologies and Applications, 2011, 10 (2), pp. 189 - 202
Issue Date:: 2011-03-01

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Field	Value	Language
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	International Journal of Intelligent Systems Technologies and Applications, 2011, 10 (2), pp. 189 - 202	en_US
dc.identifier.issn	1740-8865	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18264
dc.description.abstract	In a real time, automatic licence plate recognition system, licence detection, character segmentation and character recognition are three important components. All these three components generally require high accuracy and fast recognition speed to process. In this paper, general processing steps for license plate recognition (LPR) are addressed. After three types of combined classifiers are introduced and compared, a hierarchically combined classifier is designed based on an inductive learning-based method and an support vector machine (SVM)-based classification. This approach employs the inductive learning-based method to roughly divide all classes into smaller groups. Then, the SVM approach is used for character classification in individual groups. Having obtained a collection of samples of characters in advance from licence plates after licence detection and character segmentation steps, some known samples are available for training. After the training process, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for next fast training and testing processes based on SVMs. The experimental results show that the hierarchically combined classifier is better than either the inductive learning-based classification or the SVM-based classification with a lower error rate and a faster processing speed. © 2011 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Intelligent Systems Technologies and Applications	en_US
dc.relation.isbasedon	10.1504/IJISTA.2011.039019	en_US
dc.title	A system for licence plate recognition using a hierarchically combined classifier	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	10	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

In a real time, automatic licence plate recognition system, licence detection, character segmentation and character recognition are three important components. All these three components generally require high accuracy and fast recognition speed to process. In this paper, general processing steps for license plate recognition (LPR) are addressed. After three types of combined classifiers are introduced and compared, a hierarchically combined classifier is designed based on an inductive learning-based method and an support vector machine (SVM)-based classification. This approach employs the inductive learning-based method to roughly divide all classes into smaller groups. Then, the SVM approach is used for character classification in individual groups. Having obtained a collection of samples of characters in advance from licence plates after licence detection and character segmentation steps, some known samples are available for training. After the training process, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for next fast training and testing processes based on SVMs. The experimental results show that the hierarchically combined classifier is better than either the inductive learning-based classification or the SVM-based classification with a lower error rate and a faster processing speed. © 2011 Inderscience Enterprises Ltd.

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

http://hdl.handle.net/10453/18264