Ancient Script Deciphering Using Generative AI

Deciphering ancient scripts has always been a challenging task due to language extinction, script deterioration, and the lack of contextual references. Traditional methods rely heavily on linguistic experts and archaeological findings, making the process slow and resource-intensive. This research explores the use of Generative AI models to automate and accelerate the decipherment process. By training AI models on datasets containing ancient texts and their modern translations, we aim to develop a system capable of generating meaningful translations for lost scripts. The system utilizes Transformer- based architectures, which have proven effective in language modeling and text generation. Experimental results show that AI can recognize script patterns, infer missing text, and provide preliminary translations, reducing reliance on manual methods. While the model performs well for well- documented scripts, challenges remain in handling incomplete or poorly preserved texts. Future improvements will focus on refining model accuracy, expanding datasets, and incorporating expert feedback for more reliable translations.