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Abstract This paper presents an efficient and layout‐independent Automatic License Plate Recognition (ALPR) system based on the state‐of‐the‐art you only look once (YOLO) object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post‐processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade‐off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end‐to‐end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR‐EU, SSIG‐SegPlate and UFPR‐ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. The authors' system also achieved impressive frames per second (FPS) rates on a high‐end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that the authors manually labelled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.
Laroca et al. (Sun,) studied this question.