From Clean to Corroded: Prompt-Guided Diffusion Inpainting for License Plate Degradation

Traditional OCR and Automatic Number Plate Recognition (ANPR) systems typically perform well under controlled conditions, such as clean, well-lit, high-resolution environments. However, their reliability diminishes in real-world scenarios involving visual degradations like dust, occlusions, rust, glare, or faded characters. To address this, we present a novel imaging pipeline that synthetically renders number plates under challenging conditions using diffusion-based generative models. Our approach integrates edge-conditioned ControlNet with inpainting-driven diffusion to generate spatially precise and semantically realistic distortions. The process is guided by using tailored text prompts generated via a large language model, enabling contextual degradation synthesis. A hybrid architecture combines SDXL and a stable inpainting backbone to operate on full-frame vehicle images, capturing variability in viewpoint, distance, and background clutter. We validate the effectiveness of our synthetic dataset by evaluating the performance of a mature OCR engine (EasyOCR), showing that standard models struggle with complex distortions—highlighting the potential of our method for training and benchmarking robust OCR systems. The complete synthetic number plate degradation dataset and the fine-tuned model, is available at (https://github.com/MAli-Farooq/Prompt-Guided-Diffusion-Inpainting-for-License-Plate-Degradation/).