License Plate Images Generation with Diffusion Models

The paper introduces using diffusion models for realistic license plate image generation, addressing privacy constraints. A DDPM was trained on a Ukrainian dataset, producing 1000 synthetic images for analysis. Manual classification and annotation revealed insights into model outputs, including success rates, character distributions, and failure types. The study validates diffusion models' efficacy for license plate synthesis, offering a synthetic dataset for traffic management tasks. Incorporating pseudolabeled synthetic data improved LPR accuracy by 3% compared to baseline.