Reviving the Past: How Generative Adversarial Networks (GANs) are Empowering Digital Archives to Restore Old Documents

Throughout history, humanity has produced a vast repository of knowledge, preserved in documents that have become invaluable sources of information for researchers, historians, and curious individuals alike. However, many of these documents are fragile and deteriorating, posing a significant challenge to their long-term preservation and accessibility. Generative adversarial networks (GANs), a revolutionary class of artificial intelligence algorithms, are emerging as a powerful tool for addressing this challenge and restoring these precious artifacts. GANs are a type of unsupervised learning system that pits two neural networks against each other in a competitive game. One network, the generator, is tasked with creating new data that resembles the training data, while the other network, the discriminator, attempts to distinguish between real and generated data. This adversarial process drives the generator to become increasingly adept at producing realistic outputs. <a href="https://medium.com/@arita111997/reviving-the-past-how-generative-adversarial-networks-gans-are-empowering-digital-archives-to-7bbaea4e4bd1">Visit Now</a>