Number plate recognition based on support vector machines

Lihong, Z; Xiangjian, H

Number plate recognition based on support vector machines

Lihong, Z Xiangjian, H

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, 2006
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Lihong, Z	en_US
dc.contributor.author	Xiangjian, H https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, 2006	en_US
dc.identifier.isbn	0769526888	en_US
dc.identifier.isbn	9780769526881	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2952
dc.description.abstract	Automatic number plate recognition method is required due to increasing traffic management. In this paper, we first briefly review some knowledge of Support Vector Machines (SVMs). Then a number plate recognition algorithm is proposed. This algorithm employs an SVM to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. The recognition results are achieved by finding the maximum value between the outputs of SVMs. In this paper, experimental results based on SVMs are given. From the experimental results, we can make the conclusion that SVM is better than others such as inductive learning-based number recognition © 2006 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation.ispartof	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006	en_US
dc.relation.isbasedon	10.1109/AVSS.2006.82	en_US
dc.title	Number plate recognition based on support vector machines	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Sydney, Australia	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Automatic number plate recognition method is required due to increasing traffic management. In this paper, we first briefly review some knowledge of Support Vector Machines (SVMs). Then a number plate recognition algorithm is proposed. This algorithm employs an SVM to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. The recognition results are achieved by finding the maximum value between the outputs of SVMs. In this paper, experimental results based on SVMs are given. From the experimental results, we can make the conclusion that SVM is better than others such as inductive learning-based number recognition © 2006 IEEE.

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http://hdl.handle.net/10453/2952