Virtually Unrolling the Herculaneum Papyri by Diffeomorphic Spiral Fitting
This work addresses the challenge of accessing ancient texts in severely damaged scrolls for archaeologists and historians, representing an incremental improvement over prior automated methods.
The paper tackles the problem of automatically tracing the papyrus surface in CT scans of fragile Herculaneum scrolls for virtual unrolling, achieving successful unrolling of large regions and outperforming the only existing automated method suitable for this data.
The Herculaneum Papyri are a collection of rolled papyrus documents that were charred and buried by the famous eruption of Mount Vesuvius. They promise to contain a wealth of previously unseen Greek and Latin texts, but are extremely fragile and thus most cannot be unrolled physically. A solution to access these texts is virtual unrolling, where the papyrus surface is digitally traced out in a CT scan of the scroll, to create a flattened representation. This tracing is very laborious to do manually in gigavoxel-sized scans, so automated approaches are desirable. We present the first top-down method that automatically fits a surface model to a CT scan of a severely damaged scroll. We take a novel approach that globally fits an explicit parametric model of the deformed scroll to existing neural network predictions of where the rolled papyrus likely passes. Our method guarantees the resulting surface is a single continuous 2D sheet, even passing through regions where the surface is not detectable in the CT scan. We conduct comprehensive experiments on high-resolution CT scans of two scrolls, showing that our approach successfully unrolls large regions, and exceeds the performance of the only existing automated unrolling method suitable for this data.