Papers › Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
Zhenbo Xu, Wei Yang, Ajin Meng, Nanxue Lu, Huan Huang, Changchun Ying, Liusheng Huang
Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets. In this paper, we introduce CCPD, a large and comprehensive LP dataset. All images are taken manually by workers of a roadside parking management company and are annotated carefully. To our best knowledge, CCPD is the largest publicly available LP dataset to date with over 250k unique car images, and the only one provides vertices location annotations. With CCPD, we present a novel network model which can predict the bounding box and recognize the corresponding LP number simultaneously with high speed and accuracy. Through comparative experiments, we demonstrate our model outperforms current object detection and recognition approaches in both accuracy and speed. In real-world applications, our model recognizes LP numbers directly from relatively high-resolution images at over 61 fps and 98.5% accuracy.
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