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.

翻译：车牌图像复原的重要性不仅体现在车牌识别（LPR）系统的预处理阶段，它还具有多种用途，包括提升证据价值、增强视觉界面的清晰度以及促进车牌图像的进一步利用。我们提出了一种新颖的、具有字符级引导的扩散框架——CharDiff，它能有效复原并识别在真实条件下捕获的严重退化车牌图像。CharDiff利用了通过外部分割模块和专为低质量车牌图像定制的光学字符识别（OCR）模块提取的细粒度字符级先验信息。为了实现精确且聚焦的引导，CharDiff引入了一个新颖的基于区域掩码的字符引导注意力（CHARM）模块，该模块确保每个字符的引导仅限于其自身区域，从而避免对其他区域产生干扰。在实验中，CharDiff在复原质量和识别准确率方面均显著优于基线复原模型，在Roboflow-LP数据集上，与性能最佳的基线模型相比，实现了28%的字符错误率（CER）相对降低。这些结果表明，结构化的字符引导条件化有效增强了基于扩散的车牌复原与识别在实际部署场景中的鲁棒性。