Real time character recognition of car number plates

Zheng, Lihong

Real time character recognition of car number plates

Zheng, Lihong

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Publication Type:: Thesis
Issue Date:: 2008

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	02Whole.pdf	thesis	49.15 MB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Zheng, Lihong
dc.date.accessioned	2016-03-29T03:05:04Z
dc.date.available	2016-03-29T03:05:04Z
dc.date.issued	2008
dc.identifier.uri	http://hdl.handle.net/10453/43404
dc.description	University of Technology, Sydney. Faculty of Information Technology.	en_AU
dc.description	NO FULL TEXT AVAILABLE. Access is restricted indefinitely. The hardcopy may be available for consultation at the UTS Library.
dc.description.abstract	NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Pattern recognition is an old and also an ongoing research area that studies the operation and design of systems that recognize patterns in data. Many powerful tools have been explored for various real-time applications such as image analysis, character recognition, speech analysis, human and machine diagnostics, person identification, industrial inspection and so on. Aiming at general objectives of real time character recognition, namely fast processing speed, high accuracy and low cost, this thesis focuses on character recognition of car number plates and aims to recognize the characters on number plate images quickly and accurately. Two different learning based methods, inductive learning and SVM based classifiers, are presented. A new combined recognition system for character recognition of car number plates is also proposed. The inductive learning based method is applied to roughly divide the candidate into primary groups corresponding to digits and letters. Then SVM based approach is used to recognize the characters in the groups accurately. Furthermore, a classification tree is proposed that provides an efficient way both in training and testing processes. Computational time is therefore saved. This approach differs from other approaches such as template matching, OCR and Neural Network based approaches. It shows efficient performance in terms of both accuracy and speed, two key indices for real time applications. Furthermore, for those number plates which were misclassified due to poor quality of number plates, a new approach of character recognition without segmentation has also been developed for quick and accurate recognition of car number plates. It achieves the perfect 100% accuracy rate on our database. In addition, based on a new image structure-Spiral Architecture (SA), modified feature masks and modified inductive learning approach are presented. A conclusion is drawn that number recognition on SA performs better than on the traditional image structure.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Real time character recognition of car number plates	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	closed_access

Abstract:

NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Pattern recognition is an old and also an ongoing research area that studies the operation and design of systems that recognize patterns in data. Many powerful tools have been explored for various real-time applications such as image analysis, character recognition, speech analysis, human and machine diagnostics, person identification, industrial inspection and so on. Aiming at general objectives of real time character recognition, namely fast processing speed, high accuracy and low cost, this thesis focuses on character recognition of car number plates and aims to recognize the characters on number plate images quickly and accurately. Two different learning based methods, inductive learning and SVM based classifiers, are presented. A new combined recognition system for character recognition of car number plates is also proposed. The inductive learning based method is applied to roughly divide the candidate into primary groups corresponding to digits and letters. Then SVM based approach is used to recognize the characters in the groups accurately. Furthermore, a classification tree is proposed that provides an efficient way both in training and testing processes. Computational time is therefore saved. This approach differs from other approaches such as template matching, OCR and Neural Network based approaches. It shows efficient performance in terms of both accuracy and speed, two key indices for real time applications. Furthermore, for those number plates which were misclassified due to poor quality of number plates, a new approach of character recognition without segmentation has also been developed for quick and accurate recognition of car number plates. It achieves the perfect 100% accuracy rate on our database. In addition, based on a new image structure-Spiral Architecture (SA), modified feature masks and modified inductive learning approach are presented. A conclusion is drawn that number recognition on SA performs better than on the traditional image structure.

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http://hdl.handle.net/10453/43404