License plate (LP) recognition is crucial in intelligent traffic management systems. However, factors such as long distances and poor camera quality often lead to severe degradation of captured LP images, posing challenges to accurate recognition. The design of License Plate Image Restoration (LPIR) methods frequently relies on synthetic degraded data, which limits their effectiveness on real-world severely degraded LP images. To address this issue, we introduce the first paired LPIR dataset collected in real-world scenarios, named MDLP, including 10,245 pairs of multi-frame severely degraded LP images and their corresponding clear images. To better restore severely degraded LP, we propose a novel Diffusion-based network, called LP-Diff, to tackle real-world LPIR tasks. Our approach incorporates (1) an Inter-frame Cross Attention Module to fuse temporal information across multiple frames, (2) a Texture Enhancement Module to restore texture information in degraded images, and (3) a Dual-Pathway Fusion Module to select effective features from both channel and spatial dimensions. Extensive experiments demonstrate the reliability of our dataset for model training and evaluation. Our proposed LP-Diff consistently outperforms other state-of-the-art image restoration methods on real-world LPIR tasks. Our dataset and code will be released after the paper is accepted to facilitate reproducibility and future research.