  Forensic license plate recognition (FLPR) remains an open challenge in legal
contexts such as criminal investigations, where unreadable license plates (LPs)
need to be deciphered from highly compressed and/or low resolution footage,
e.g., from surveillance cameras. In this work, we propose a side-informed
Transformer architecture that embeds knowledge on the input compression level
to improve recognition under strong compression. We show the effectiveness of
Transformers for license plate recognition (LPR) on a low-quality real-world
dataset. We also provide a synthetic dataset that includes strongly degraded,
illegible LP images and analyze the impact of knowledge embedding on it. The
network outperforms existing FLPR methods and standard state-of-the art image
recognition models while requiring less parameters. For the severest degraded
images, we can improve recognition by up to 8.9 percent points.
