Towards a Dataset for Paleographic Details in Historical Torah Scrolls

Historical textual witnesses used in religious practice have been a research interest for a long time but still remain mysterious. In particular, medieval Torah scrolls show irregularities in the scripture, whose intentions have not yet been revealed. In this paper, we assess the analysis of letter decorations from the perspective of computer vision and investigate the possibilities of extending qualitative research in Jewish Studies by quantitative analysis methods of computer science. For this purpose, we introduce a methodological approach to obtain a reproducible and extensible dataset of Hebrew letters and present a set of labels usable for various machine learning tasks. The evaluation of the dataset in terms of decoration recognition shows promising prediction accuracy rates of up to 90% with standard transfer learning methods and architectures.