Computer Science > Computer Vision and Pattern Recognition
[Submitted on 29 Jul 2022 (v1), last revised 3 May 2024 (this version, v3)]
Title:Forensic License Plate Recognition with Compression-Informed Transformers
View PDF HTML (experimental)Abstract: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.
Submission history
From: Denise Moussa [view email][v1] Fri, 29 Jul 2022 13:58:24 UTC (16,429 KB)
[v2] Fri, 16 Sep 2022 13:45:56 UTC (16,429 KB)
[v3] Fri, 3 May 2024 15:15:27 UTC (1,039 KB)
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