This talk presents Palimpsest Manuscripts Reconstruction Generative Adversarial Network, a novel framework for restoring the original forms of input palimpsests; it removes the over-text and refills the missing gaps in the text and background in an end-to-end manner. The structure and attributes of the under-text are encoded using reference patches. The generator network combines the encoded reference with an input palimpsest patch and restores the original form. To train our model, we synthesize palimpsests that mimic the attributes of the original ones. We compare the performance of our model with the state-of-art models using five different evaluation metrics, such as PSNR and SSIM. We show that our approach not only achieves state-of-the-art performance in terms of PSNR/SSIM metrics but also significantly improves the visual quality of the restored images.