Beáta Megyesi is a Professor of Computational Linguistics at Stockholm University. She completed her studies at Stockholm University and earned her doctorate from the Royal Institute of Technology in 2002. In 2003, she joined Uppsala University, where she advanced from assistant professor to full professor by 2021. Over the course of her academic career, she has published more than 100 publications and secured nearly 100 million SEK in external funding. She has served as Chair of the Swedish Research Council's Linguistics Review Panel, President of the Northern European Association for Language Technology, and Head of the Department of Linguistics and Philology at Uppsala University. She is currently the Principal Investigator of the DECRYPT project (2018–2025) on historical cryptology financed by the Swedish Research Council and leads the DESCRYPT: Echoes of History Analysis and Decipherment of Historical Writings program funded by Riksbankens Jubileumsfond (2025–2032).

Manuscripts written in rare or unknown scripts represent a largely untapped reservoir of historical and cultural knowledge, yet their study is frequently sidelined due to the multifaceted challenges they present. These texts, characterized by unique linguistic structures and diverse symbol sets, demand an interdisciplinary approach that spans linguistic analysis, paleography, cryptanalysis, and cultural studies. While recent advancements in artificial intelligence have introduced promising tools for automating tasks such as identification and transcription, the nuanced interpretation and verification of these manuscripts remain firmly in the realm of human expertise. In this talk, I will explore the inherent complexities of working with rare scripts, discuss the current state of automation in manuscript analysis, and argue for the development of hybrid systems that combine AI efficiency with expert intervention. By enabling minimal corrective inputs and adapting models to various handwriting styles and script idiosyncrasies, such systems have the potential to bridge the gap between computational capabilities and the specialized domain knowledge required for meaningful historical interpretation.