High-Fidelity Text Refinement for ControlNet-Guided Latent Diffusion in Document Inpainting

Blind document image inpainting (DII) aims to restore degraded document scans without prior knowledge of noise locations, yet existing methods either produce over-smoothed text or introduce pseudo-characters. We propose a novel high-fidelity, text-guided restoration framework based on a ControlNet-guided Latent Diffusion Model (CGLDM). First, we extract and refine noisy OCR outputs using a two-stage Vision Language Model (VLM) and Large Language Model (LLM) pipeline, leveraging both global document context and local text cues to deliver near ground-truth textual fidelity. Next, these refined text features, together with an initial visually restored image, condition a latent diffusion process that progressively denoises and reconstructs clean image latents. To suppress patch-wise background inconsistencies inherent in high-resolution processing, we introduce an explicit feature-alignment loss of diffusion model training that enforces agreement between the predicted features and the VAE-encoded features of the ground-truth image. Extensive experiments on the FUNSD-ZH dataset demonstrate that our approach outperforms state-of-the-art methods in OCR legibility and maintains competitive image-level fidelity.