Restoration and Enhancement of Historical Manuscript Images Using Diffusion Model

Historical manuscripts suffer from various forms of degradation such as ink bleed, fading, and physical damage, which impairs their readability and usability. This study proposes a novel image restoration framework using Diffusion Probabilistic Models (DPMs) enhanced with OCR-guided finetuning. By integrating a two-stage architecture-comprising a NAFNet-based Initial Predictor and a Diffusion-Based De-noiser-this system restores both low- and high-frequency document details. Evaluations on benchmark datasets demonstrate that the proposed method outperforms traditional techniques in terms of PSNR, SSIM, and OCR accuracy, making it a scalable and robust solution for cultural heritage preservation.