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• W4361763772 abstract "In this paper, we adopt a powerful deep neural network (DNN) to tackle the problems of detecting car license plates (LPs) and recognizing Arabic letters in natural scene images. Our Automatic License Plate Recognition (ALPR) system is built using state-of-the-art methodologies and techniques to provide the optimal speed/accuracy trade-off at each level. Images of vehicles and Moroccan and European LPs were used to train the neural networks. The system was able to correctly detect and recognize in the test set all the characters existing in a LP with an accuracy of 91.11% and 98.89% taking into account that only one character is misread. Using a high-end GPU, our system likewise produced outstanding real-time execution results." @default.
• W4361763772 created "2023-04-04" @default.
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• W4361763772 date "2023-01-01" @default.
• W4361763772 modified "2023-09-27" @default.
• W4361763772 title "An Efficient Real-Time Moroccan Automatic License Plate Recognition System Based on the YOLO Object Detector" @default.
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• W4361763772 doi "https://doi.org/10.1007/978-3-031-28387-1_25" @default.
• W4361763772 hasPublicationYear "2023" @default.
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