Performance of Automatic License Plate Recognition Systems on Distorted Images

Automatic License Plate Recognition (ALPR) systems are essential for various applications, including law enforcement, traffic management, and access control. However, their performance can be significantly affected by image distortion in adverse environmental conditions and the imaging pipeline. Three different ALPR systems were used to evaluate their robustness to different distortions using images from six well-known ALPR datasets. Two groups of distortions were the focus of our study: simulated weather conditions (rain, brightness, fog, frost, and snow), and modeled camera read noise in the simulated imaging pipeline. Results indicate that certain weather distortions drastically reduced the accuracy of ALPR systems, with the accuracy of the systems approaching zero in some cases. Read noise also negatively impacted performance, even at minimal levels. The sensitivity to the introduced distortions varied between different models and datasets. The results underscore the need for robust ALPR system designs that can handle diverse and challenging capturing conditions.