CharDiff: A Diffusion Model with Character-Level Guidance for License Plate Image Restoration
This work addresses license plate image restoration for enhancing License Plate Recognition (LPR) systems and other applications, representing an incremental improvement with domain-specific impact.
The paper tackled the problem of restoring severely degraded license plate images under realistic conditions by proposing CharDiff, a diffusion model with character-level guidance, which achieved a 28% relative reduction in character error rate (CER) on the Roboflow-LP dataset compared to the best baseline.
The significance of license plate image restoration goes beyond the preprocessing stage of License Plate Recognition (LPR) systems, as it also serves various purposes, including increasing evidential value, enhancing the clarity of visual interface, and facilitating further utilization of license plate images. We propose a novel diffusion-based framework with character-level guidance, CharDiff, which effectively restores and recognizes severely degraded license plate images captured under realistic conditions. CharDiff leverages fine-grained character-level priors extracted through external segmentation and Optical Character Recognition (OCR) modules tailored for low-quality license plate images. For precise and focused guidance, CharDiff incorporates a novel Character-guided Attention through Region-wise Masking (CHARM) module, which ensures that each character's guidance is restricted to its own region, thereby avoiding interference with other regions. In experiments, CharDiff significantly outperformed the baseline restoration models in both restoration quality and recognition accuracy, achieving a 28% relative reduction in CER on the Roboflow-LP dataset, compared to the best-performing baseline model. These results indicate that the structured character-guided conditioning effectively enhances the robustness of diffusion-based license plate restoration and recognition in practical deployment scenarios.