Efficient character segmentation on car license plates

Zheng, L; Gao, J; He, X

Efficient character segmentation on car license plates

Zheng, L Gao, J He, X

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Publication Type:: Conference Proceeding
Citation:: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 1139 - 1144
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	Gao, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 1139 - 1144	en_US
dc.identifier.isbn	9781424478132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16410
dc.description.abstract	In this paper an improved hill climbing algorithm based method is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of two different types of image segmentation approaches, the hill climbing algorithm based method gave a better image segmentation results. The hill climbing algorithm was modified by introducing automatic parameter determination and smart searching. After modification it efficiently detects the peaks (local maxima) that represent different clusters in the global histogram of an image. The process is successful by getting a clean license plate image removing all unwanted areas. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate after non-character areas are removed. The recognition rate increased from about 30.6% before our proposed process to about 91.3% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved. ©2010 IEEE.	en_US
dc.relation.ispartof	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010	en_US
dc.relation.isbasedon	10.1109/ICARCV.2010.5707938	en_US
dc.title	Efficient character segmentation on car license plates	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Singapore	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:

In this paper an improved hill climbing algorithm based method is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of two different types of image segmentation approaches, the hill climbing algorithm based method gave a better image segmentation results. The hill climbing algorithm was modified by introducing automatic parameter determination and smart searching. After modification it efficiently detects the peaks (local maxima) that represent different clusters in the global histogram of an image. The process is successful by getting a clean license plate image removing all unwanted areas. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate after non-character areas are removed. The recognition rate increased from about 30.6% before our proposed process to about 91.3% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved. ©2010 IEEE.

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http://hdl.handle.net/10453/16410