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Content Provider | IEEE Xplore Digital Library |
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Author | Zhong Qin Shengli Shi Jianmin Xu Hui Fu |
Copyright Year | 2006 |
Description | Author affiliation: Coll. of Traffic & Commun., Southern China Univ. of Technol., Guangzhou (Zhong Qin; Shengli Shi; Jianmin Xu; Hui Fu) |
Abstract | License plate location (LPL) plays an important role in the license plate recognition (LPR). The license plate characters contain abundant corners which distribute intensively and regularly. Accord to the feature, a novel method to locate the license plate accurately and fast is proposed in this paper. The method is dived into three steps which are corners detection, finding out candidate license plates and locating the license plate accurately. In the course of locating license plate, three thresholds are used. First, the reasonable threshold is used to eliminate a great number of non-corner pixels to speedup corner-detections. Second, a big threshold is selected with a slid window to find out candidates. Finally, the region of candidate license plates are expanded, all the corners in it were stand out with a smaller threshold, and the license plate is located accurately using clustering analyses. Experiments have been conducted, 1200 license plate images are tested, the accuracy is 98.42% and the average consuming time is 20ms |
Starting Page | 8645 |
Ending Page | 8649 |
File Size | 319575 |
Page Count | 5 |
File Format | |
ISBN | 1424403324 |
DOI | 10.1109/WCICA.2006.1713668 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-06-21 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Licenses Space technology Vehicle detection Intelligent systems Intelligent vehicles Image segmentation Image edge detection Character recognition Space vehicles Neural networks Corner Detection License Plate Location intelligence traffic systems License Plate Recognition |
Content Type | Text |
Resource Type | Article |
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