Reading the Herculaneum papyri is challenging because both the scrolls and the ink, which is carbon-based, are carbonized. In X-ray radiography and tomography, ink detection typically relies on density- or composition-driven contrast, but carbon ink on carbonized papyrus provides little attenuation contrast. Building on earlier X-ray phase contrast tomographic and microscopic observations suggesting that relief contributes to letter visibility, we show that surface morphology of written regions contains enough signal to distinguish ink from papyrus. To this end, we train a deep-learning model on three-dimensional optical profilometry of mechanically opened Herculaneum papyri to separate inked and uninked areas. Although our measurements do not show general bulk letter relief or universal roughness cues, we show that high-resolution topography alone contains a usable signal for ink detection on the collected dataset. We further quantify how lateral sampling governs learnability and how a native-resolution model behaves on coarsened inputs. Leave-one-papyrus-out transfer is heterogeneous. Diminishing segmentation performance with decreasing lateral resolution provides insight into the characteristic spatial scales that must be resolved on our dataset to exploit the morphological signal. These findings define a proof-of-concept and conditionally inform resolution needs for morphology-based reading of closed scrolls, which will also depend on the imaging modality.
Angelotti et al. (Thu,) studied this question.