Egyptian car plate recognition based on YOLOv8, Easy-OCR, and CNN

Amany Sarhan, Rowyda Abdel-Rahem, Bassel Darwish, Arwa Abou-Attia, Ahmed Sneed, Shahd Hatem, Awatef Badran, Mohamed Ramadan
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引用次数: 0

Abstract

This research presents an innovative approach to Egyptian car plate recognition using YOLOv8 and optical character recognition (OCR) technologies. Leveraging the powerful object detection capabilities of YOLOv8, the system efficiently detects car plates within images, videos, or real-time. Subsequently, OCR algorithms are applied to extract alphanumeric characters from the identified plates, facilitating accurate license plate recognition. The integration of YOLOv8 and OCR enhances the system's robustness in varying conditions, contributing to improved performance in real-world scenarios. This study advances the field of automatic license plate recognition, showcasing the potential for practical applications in traffic management, law enforcement, and security systems. A public dataset of Egyptian car plates is used for training and testing the model. Two OCR approaches are used and tested which proved their performance, while CNN-based approach reaches 99.4% accuracy.
基于 YOLOv8、Easy-OCR 和 CNN 的埃及车牌识别技术
这项研究提出了一种利用 YOLOv8 和光学字符识别 (OCR) 技术进行埃及车牌识别的创新方法。利用 YOLOv8 强大的对象检测功能,该系统可以高效地检测图像、视频或实时图像中的车牌。随后,OCR 算法将从识别出的车牌中提取字母数字字符,从而促进车牌的准确识别。YOLOv8 和 OCR 的集成增强了系统在不同条件下的鲁棒性,有助于提高实际应用中的性能。这项研究推动了车牌自动识别领域的发展,展示了其在交通管理、执法和安全系统中的实际应用潜力。模型的训练和测试使用了埃及车牌的公共数据集。使用并测试了两种 OCR 方法,证明了它们的性能,而基于 CNN 的方法达到了 99.4% 的准确率。
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