Mapping Scribal Landscapes of Tibetan and Sanskrit Manuscripts

MASLOTS uses deep learning (DL) methods to systematically identify the hands of individual scribes in manuscript collections. Using this new information alongside the other available sources, researchers in MASLOTS evaluate and investigate the roles of the historical figures traceable in the manuscripts. With this approach, MASLOTS has the potential to generate a new understanding about how Buddhist ideas were written down, transmitted, and interpreted by individuals, besides opening up new research perspectives on the processes of dissemination and adaptation of Buddhism in historical milieus. In addition, MASLOTS proposes to explore the possibilities as well as the limits of combining computational and traditional methods for any future, text-based work on the history of Buddhism.

MASLOTS focuses on two groups of manuscripts as case studies. The groups are from two different historical periods that were both crucial for the spread of Buddhism across Asia. The first group, contained within the larger collection of Dunhuang Tibetan manuscripts, consists of manuscripts possibly written by Chos-grub (fl. ninth century CE). The second group consists of manuscripts of Indian Buddhist scholars who, during the early thirteenth century CE, made lasting contributions to Buddhist religion and philosophy in India, Tibet, and Nepal.

MASLOTS is a cooperation between the Austrian Academy of Sciences’ Institute for the Cultural and Intellectual History of Asia (https://www.oeaw.ac.at/en/ikga) and the TU Wien’s Computer Vision Lab (https://cvl.tuwien.ac.at/).

Partners:

• Austrian Academy of Sciences, Institute for the Cultural and Intellectual History of Asia