DocumentCode :
691852
Title :
A New Method for License Plate Characters Recognition Based on Sliding Window Search
Author :
Ying-Jia Bu ; Mei Xie
Author_Institution :
Sch. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear :
2013
fDate :
21-22 Dec. 2013
Firstpage :
304
Lastpage :
307
Abstract :
In this paper, a new system for license plate characters recognition is proposed, on the basis of a novel characters recognition algorithm called sliding window search. This system includes license plate location, license plate characters pre-segmentation and characters recognition based on sliding window search. The Gaussian mixture model is used for background modeling and a Bayesian classifier based on the target tracking technology is used to monitor all vehicles within the scene. The brightness difference between the license plate characters and background is considered. Based on the priori information about the characters width, the license plate characters pre-segmentation is easily acquired. Finally, sliding window search is conducted on the license plate characters. Compared with other license plate characters recognition systems, this system has a high recognition accuracy and has good robustness even in a complex environment.
Keywords :
Gaussian processes; belief networks; image classification; image segmentation; mixture models; optical character recognition; traffic engineering computing; Bayesian classifier; background modeling; license plate characters presegmentation; license plate characters recognition; license plate location; presegmentation Gaussian mixture model; sliding window search; target tracking technology; Conferences; license plate characters pre-segmentation; license plate characters recognition; sliding window search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Dependable, Autonomic and Secure Computing (DASC), 2013 IEEE 11th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-3380-8
Type :
conf
DOI :
10.1109/DASC.2013.79
Filename :
6844379
Link To Document :
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