A Robust and Efficient Approach to License Plate Detection

Abstract-This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without
sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization. Moreover, a cascaded license plate classifier based on linear SVMs using color saliency features is introduced to identify the true license plate from among the candidate regions. For performance evaluation, a dataset consisting of 3828 images captured from diverse scenes under different conditions is also presented. Extensive experiments on the widely used Caltech license plate dataset and our newly introduced dataset demonstrate that the proposed approach substantially outperforms state-ofthe-art methods in terms of both detection accuracy and run-time efficiency, increasing the detection ratio from 91.09% to 96.62% while decreasing the run time from 672 ms to 42 ms for processing an image with a resolution of 1082 ×728. The executable code and
our collected dataset are publicly available.