Development of a Nigerian Vehicle License Plate Recognition System Using Deep Learning

License Plate Recognition (LPR) is a technology that combines object detection and optical character recognition (OCR) to automatically identify vehicles by their license plates. This study explores the development and evaluation of an LPR system using deep learning techniques. The system was trained and tested on a dataset of 1000 car images, with annotations provided using Label Studio. Various object detection models, including InceptionResNetV2, MobileNetV2, InceptionV3, and YOLOv8, were evaluated for their accuracy and efficiency. YOLOv8 emerged as the most suitable model due to its superior performance, achieving high precision, recall, and mAP (mean Average Precision) metrics. The study also investigated the challenges of character recognition in low-resolution images and explored the integration of a Super resolution Generative Adversarial Network (SRGAN) with Tesseract-OCR to enhance character recognition accuracy. The findings of this research contribute to the advancement of LPR technology and its potential applications in traffic management, security, and law enforcement.