This paper presents an efficient and layout-independent Automatic License
Plate Recognition (ALPR) system based on the state-of-the-art 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. Our 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 we manually
labeled 38,351 bounding boxes on 6,239 images from public datasets and made the
annotations publicly available to the research community