Robust license plate segmentation method based on texture features and radon transform

Authors:
Jun Kong;Xinyue Liu;YingHua Lu;Xiaofeng Zhou;Qiushi Zhao
Affiliations:
Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

A robust method for plate segmentation in a License Plate Recognition system is presented in this paper, the proposed approach is designed to work in a wide range of acquisition conditions, including unrestricted scene environments, lighting conditions, viewing points and camera-to-car distance. Experiments have been preformed to prove the robustness and accuracy of the approach. The experiment results show that almost 96.2% of input images are correctly segmented on the average. Because our algorithm has fast speed and needs little memory space, it can be used in real time system.