Ink Detection from Surface Topography of the Herculaneum Papyri

2026-03-29

AI Summary

This study addresses the challenge of reading Herculaneum scrolls, whose carbon-based ink is indistinguishable from the carbonized papyrus substrate in X-ray imaging due to insufficient compositional or density contrast. The authors hypothesize that inked regions exhibit discernible surface topography and, for the first time, employ high-resolution 3D optical profilometry to acquire detailed morphological data. A machine learning model trained on this data successfully segments inked from non-inked areas based solely on surface topography. The results demonstrate that carbon-based ink can be effectively detected through morphological cues alone, with detection performance critically dependent on lateral sampling resolution. This work establishes a quantitative relationship between the learnability of topographic signals and their spatial scale, providing crucial guidance for designing the resolution parameters of X-ray tomography in the non-invasive reading of sealed ancient scrolls.

Abstract

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 the morphological hypothesis, we show that the surface morphology of written regions contains enough signal to distinguish ink from papyrus. To this end, we train machine learning models on three-dimensional optical profilometry from mechanically opened Herculaneum papyri to separate inked and uninked areas. We further quantify how lateral sampling governs learnability and how a native-resolution model behaves on coarsened inputs. We show that high-resolution topography alone contains a usable signal for ink detection. 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 inform spatial resolution targets for morphology-based reading of closed scrolls through X-ray tomography.

Problem

• ink detection
• Herculaneum papyri
• carbon-based ink
• surface topography
• X-ray tomography

Innovation

• surface topography
• machine learning
• ink detection
• Herculaneum papyri
• spatial resolution