A computer vision approach towards glare mitigation and image quality enhancement in license plate recognition

Abstract

License Plate Recognition (LPR) systems play a crucial role in Intelligent Transportation Systems (ITS), facilitating automated vehicle identification for applications such as traffic monitoring, law enforcement, and toll collection. However, these systems often suffer from glare-induced distortions caused by intense light sources such as sunlight, vehicle headlights, and reflections. These distortions obscure license plate details, leading to reduced Optical Character Recognition (OCR) accuracy and compromised system reliability. This research addresses this critical challenge by developing a unified computer vision framework that integrates Autoencoders (AE) and Noise2Clean Generative Adversarial Networks (N2C-GAN) to mitigate glare and improve image quality. The study aimed to achieve four key objectives: access and utilize an existing dataset of glare-induced license plate images, image pre-processing, model implementation, and rigorous model evaluation. The proposed model demonstrated significant advances in glare mitigation, achieving a Peak Signal-to-Noise Ratio (PSNR) of 38.8 dB, a Structural Similarity Index Measure (SSIM) of 0.987, and a Visual Information Fidelity (VIF) of 0.8896. Furthermore, the model improved the accuracy of OCR to 99.9% using Google Cloud Vision OCR, underscoring its effectiveness in restoring license plate readability under glare conditions. Computational efficiency was a key focus, with a compact model size of 298 kB and a runtime of 0.7263 s, making it scalable for real-world deployment. Despite encountering limitations such as dataset bias and computational constraints, this research provides valuable insights and lays the groundwork for future advances in glare mitigation, image processing, and machine learning-based LPR enhancements. The findings have broad implications for transportation management, public safety, and automated enforcement, offering a robust solution to improve the performance and reliability of LPR systems in diverse real-world applications.