This study aims to investigate how a specific type of distortion in imaging pipelines, such as read noise, affects the performance of an automatic license plate recognition algorithm. We first evaluated a pretrained three-stage license plate recognition algorithm using undistorted license plate images. Subsequently, we applied 15 different levels of read noise using a well-known imaging pipeline simulation tool and assessed the recognition performance on the distorted images. Our analysis reveals that recognition accuracy decreases as read noise becomes more prevalent in the imaging pipeline. However, we observed that, contrary to expectations, a small amount of noise can increase vehicle detection accuracy, particularly in the case of the YOLO-based vehicle detection module. The part of the automatic license plate recognition system that is mostly prone to errors and is mostly affected by read noise is the optical character recognition module. The results highlight the importance of considering imaging pipeline distortions when designing and deploying automatic license plate recognition systems.