A Method for Segmentation, Matching and Alignment of Dead Sea Scrolls

The Dead Sea Scrolls are of great historical significance. Lamentably, in the decades since their discovery, many fragments have deteriorated. Fortunately, low-resolution grayscale infrared images of the Palestinian Archaeological Museum plates holding the scrolls in their discovered state are extant, along with recent high-quality multispectral images by the Israel Antiquities Authority. However, the necessary task of identifying each fragment in the new images on the old plates is tedious and time consuming to perform manually, and is often problematic when fragments have been moved from the original plate. We describe an automated system that segments the new and old images of fragments from the background on which they were imaged, finds their matches on the old plates and aligns and superimposes them. To this end, we developed a deep-learning based segmentation method and a cascade approach for template matching, based on scale, shape analysis and dense matching. We have tested the proposed method on five plates, comprising about 120 fragments. We present both quantitative and qualitative analyses of the results and perform an ablation study to evaluate the importance of each component of our system.