Enhancing Parking Systems with QR Code-Integrated Automatic License Plate Recognition through Convolutional Neural Networks

Abstract

This abstract describes the development and evaluation of an Automatic License Plate Recognition (ALPR) system designed to simplify the process of parking ticket generation. The traditional paradigm of manual entry of license plate information by parking personnel for exiting vehicles is replaced by the automated system proposed in this study. The system integrates a YOLO (You Only Look Once) model for the automatic recognition of license plates in vehicle images. After this initial identification, a series of pre-processing and image segmentation techniques are applied to isolate and recognize the individual digits within the license plate. A ResNet model is then used to classify the license plates. The research focuses specifically on Malaysian license plates. The experimental results show that the YOLO model recognizes license plates robustly and accurately and has a high degree of reliability. However, when validating the data set, the ResNet model achieves an accuracy of around 80 %. The study points out inherent challenges, including potential errors in segmentation, problems with non-standardized or damaged tags, and the presence of digits that may have visual similarities. In summary, while the YOLO model is reliable in recognizing license plates, the classification accuracy of the ResNet model can be further improved. Overcoming challenges such as segmentation noise and variations in license plate conditions could further optimize the overall performance of the system.