Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance

Abstract

With the ever-increasing number of vehicles on the road, a faster reliable security system for university entry is needed. This paper presents an approach for Automatic Number Plate Recognition (ANPR) using deep learning and PP-OCRv3. The proposed approach utilizes a pre-trained object detection model to locate license plates, extracts a single frame of the license plate, performs license plate recognition, applies pre-processing techniques, and employs PP-OCRv3 for text extraction in real time. The system was tested with Malaysian vehicle plates, and its accuracy and speed of detection were evaluated. The results show the system's potential to be easily adapted to different camera systems, angles, and lighting conditions by retraining the deep learning model. The paper also explores various deep learning methods, such as CenterNet, EfficientDet, and Faster R-CNN, and their effectiveness in automated vehicle registration detection. The research methodology involves creating a dataset from Open Images Dataset V4, converting label text into XML files, and utilizing the TensorFlow model trained on the COCO dataset. The paper concludes with the synthetic evaluation of the trained models, comparing their performance based on precision, recall, and F1-score. Overall, the proposed approach highlights the potential of deep learning and PP-OCRv3 in achieving accurate and efficient ANPR systems.

Keywords

• number plate detection
• optical character recognition
• license plate recognition
• tensorflow
• centernet
• efficientdet
• faster r-cnn
• pp-ocrv3