Abstract
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.
Abstract (translated)
车牌图像恢复的意义不仅限于车牌识别（LPR）系统的预处理阶段，还服务于多种目的，包括增加证据价值、提高视觉界面的清晰度和促进进一步利用车牌图像。我们提出了一种基于扩散的新框架，并结合字符级指导——CharDiff。该方法有效地修复并识别在现实条件下捕捉到的严重退化的车牌图像。 CharDiff 利用通过外部分割模块及专为低质量车牌图像设计的光学字符识别（OCR）模块提取出的精细粒度字符先验知识，从而实现高效恢复和识别功能。为了提供精确且集中的指导，CharDiff 集成了一种创新性的基于区域掩码的字符引导注意力机制（CHARM）模块，确保每个字符的指导仅限于其自身的区域内，避免对其他区域产生干扰。 实验结果显示，在图像修复质量和识别准确性方面，CharDiff 显著优于基准恢复模型。在Roboflow-LP数据集上，与表现最佳的基准模型相比，CharDiff 将CER相对降低了28%。这些结果表明，结构化的字符引导调节有效提升了基于扩散的车牌恢复和识别方法在实际部署场景中的鲁棒性。
URL
https://arxiv.org/abs/2510.17330
