Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector
Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR that has become very popular, having been downloaded more than 550 times by researchers from 76 different countries in the last year alone. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature.
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