Authors:
Mouad Bensouilah
;
Mohamed Nadjib Zennir
and
Mokhtar Taffar
Affiliation:
Department of Computer Science, Jijel University, BP 98, Ouled Aissa, 18000, Jijel, Algeria
Keyword(s):
Computer Vision, Convolutional Neural Networks, Recurrent Neural Networks, GRU, LSTM, Object Detection and Recognition, OCR, License Plates Recognition.
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%.