Paper Info
Title
Region-based license plate detection
Abstract
Automatic license plate recognition (ALPR) is one of the most important aspects of applying computer techniques towards intelligent transportation systems. In order to recognize a license plate efficiently, however, the location of the license plate, in most cases, must be detected in the first place. Due to this reason, detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of an ALPR system, which greatly affects the recognition rate and speed of the whole system. In this paper, a region-based license plate detection method is proposed. In this method, firstly, mean shift is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. Unlike other existing license plate detection methods, the proposed method focuses on regions, which demonstrates to be more robust to interference characters and more accurate when compared with other methods.
Year
DOI
Venue
2007
10.1016/j.jnca.2006.09.010
J. Network and Computer Applications
Keywords
Field
DocType
intelligent transportation system,automatic license plate recognition,Mahalanobis classifier,Mean-shift segmentation,accurate location,Region-based license plate detection,Features,proposed method,Region,license plate,existing license plate detection,region-based license plate detection,ALPR system,color vehicle image,candidate region,License plate detection
Mean shift segmentation,Computer science,Real-time computing,Intelligent transportation system,Distributed computing,License
Journal
Volume
Issue
ISSN
30
4
Journal of Network and Computer Applications
Citations 
PageRank 
References 
23
1.54
6
Authors
3
Name
Order
Citations
PageRank
Wenjing Jia132545.08
Huaifeng Zhang224018.84
Xiangjian He3932132.03