An ALPR System-based Deep Networks for the Detection and Recognition

Mouad Bensouilah, Mohamed Zennir, Mokhtar Taffar

Abstract

Automatic license plates reading (ALPR), from images or videos, is a research topic that is still relevant in the field of computer vision. In this article, we propose a new dataset and a robust ALPR system based on the YOLO object detector of literature. The trained Convolutional Neural Networks (CNN) allow us to extract features from license plates and label them through Recurrent Neural Networks (RNN) specialized character recognition. RNN are supported by GRU units instead of LSTM units that are generally used in the literature. The experiments results were conclusive reaching a recognition rate of 92%.

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Paper Citation


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An ALPR System-based Deep Networks for the Detection and Recognition
SN - 978-989-758-486-2
AU - Bensouilah M.
AU - Zennir M.
AU - Taffar M.
PY - 2021
SP - 204
EP - 211
DO - 10.5220/0010229202040211


in Harvard Style

Bensouilah M., Zennir M. and Taffar M. (2021). An ALPR System-based Deep Networks for the Detection and Recognition.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 204-211. DOI: 10.5220/0010229202040211


in Bibtex Style

@conference{icpram21,
author={Mouad Bensouilah and Mohamed Zennir and Mokhtar Taffar},
title={An ALPR System-based Deep Networks for the Detection and Recognition},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={204-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010229202040211},
isbn={978-989-758-486-2},
}