Restoration of Historical Manuscripts Using Deep Hybrid Image Processing

Historical manuscripts are invaluable cultural and intellectual assets that document the evolution of human civilization, yet many of these documents have deteriorated over time due to aging, environmental exposure, ink corrosion, biological decay, and improper storage. Traditional restoration methods, though effective to some extent, are labour-intensive, subjective, and often unable to recover severely degraded content. Recent advancements in deep image processing have opened new possibilities for automated, consistent, and highly accurate restoration of historical documents. This research proposes a comprehensive deep learning–based restoration framework that integrates convolutional neural networks (CNNs), generative adversarial networks (GANs), transformer-based enhancement models, and multi-stage fusion techniques to address various forms of degradation including noise, stains, tears, fading ink, and missing regions. The system begins with robust preprocessing for normalization and contrast correction, followed by damage detection and classification using a trained CNN. Restoration is performed through GAN-based inpainting for structural reconstruction and transformer-based refinement for recovering fine textual details. Extensive experiments conducted on large-scale datasets of digitized manuscripts demonstrate significant improvements in readability, clarity, and structural preservation compared to existing methods. Quantitative evaluations using PSNR, SSIM, and OCR accuracy confirm the superiority of the proposed model, while qualitative assessments highlight its ability to restore visually authentic manuscript textures. This study contributes a scalable and automated solution for digital preservation, offering libraries, museums, and historical archives a powerful tool to safeguard manuscripts for future generations.