  Fully Automatic License Plate Recognition (ALPR) has been a frequent research
topic due to several practical applications. However, many of the current
solutions are still not robust enough in real situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector and Normalizing flows. The model
uses two new strategies. Firstly, a two-stage network using YOLO and a
normalization flow-based model for normalization to detect Licenses Plates (LP)
and recognize the LP with numbers and Arabic characters. Secondly, Multi-scale
image transformations are implemented to provide a solution to the problem of
the YOLO cropped LP detection including significant background noise.
Furthermore, extensive experiments are led on a new dataset with realistic
scenarios, we introduce a larger public annotated dataset collected from
Moroccan plates. We demonstrate that our proposed model can learn on a small
number of samples free of single or multiple characters. The dataset will also
be made publicly available to encourage further studies and research on plate
detection and recognition.
