Ancient Textual Restoration Using Deep Neural Networks

Abstract

Ancient text restoration represents a critical area in computer science because it reflects an imagination about human life in early eras. Deep leaning plays a crucial role in AI last few years, specifically Generative Adversarial Networks (GANs), to regenerate and acclimatize old manuscripts that have suffered from the time effects, degradation, or deterioration. This work used Codex Sinaiticus dataset that preprocessed by encoding the dataset after that number and special character have been removed, new line symbol has been removed, tokenization process has been used to separate each word as an instance. Class target has been generated by removing character making it as a target and replacing it with special character. Using produces Generative Adversarial Networks (GANs), which consist of generator and discriminator inside in one learning framework. The generator part responsible for generating the missing text while the discriminator evaluates the generated text. But using an iteratively procedure these networks together collaboratively to provide a very sensitive reconstruction operations with the same format of ancient manuscripts, inscriptions and documents. Three prediction models used as proposed techniques for retrieving missing ancient texts are LSTM, RNN, and GAN and the results was validation accuracy 86%,92% and 98% respectively.