Towards End-to-End License Plate Detection
and Recognition: A Large Dataset and Baseline
Abstract. 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