Abstract
Artificial intelligence is a field that can help us, humans, to do things that are a bit difficult. In some of the fields of artificial intelligence, we find image processing. One of the most interesting applications of image processing is vehicle license plate recognition, or what is known as automatic license plate recognition (ALPR). Most of these applications use optical character recognition (OCR) methods. The Automatic License Plate Recognition system is not only focused on parking lots but can be used in all those facilities necessary to control, monitor, and have a record of all vehicles that pass through certain access. Example: private garages of companies, shopping centers, tolls, hospitals, etc. In this paper, we will explain how we applied two object detection algorithms to build a system capable of recognizing and extracting Moroccan license plates from images. We used transfer learning on YOLOv5 and Faster-RCNN and trained them both on 724 images of Moroccan registered vehicles to obtain a system that can support a parking system.
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El Ghmary, M., Ouassine, Y., Ouacha, A. (2023). Automatic License Plate Recognition with YOLOv5 and Faster-RCNN. In: Lazaar, M., En-Naimi, E.M., Zouhair, A., Al Achhab, M., Mahboub, O. (eds) Proceedings of the 6th International Conference on Big Data and Internet of Things. BDIoT 2022. Lecture Notes in Networks and Systems, vol 625. Springer, Cham. https://doi.org/10.1007/978-3-031-28387-1_30
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DOI: https://doi.org/10.1007/978-3-031-28387-1_30
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