# Ink Detection from Surface Topography of the Herculaneum Papyri

arXiv: https://arxiv.org/abs/2603.27698 · HTML: https://mlsanity.com/paper/2603.27698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 29 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [Efficient Segmentation: Learning Downsampling Near Semantic Boundaries](https://mlsanity.com/paper/1907.07156/) (2019)
- [Virtually Unrolling the Herculaneum Papyri by Diffeomorphic Spiral Fitting](https://mlsanity.com/paper/2512.04927/) (2025)

## Built on by (most cited first, 1 of 1)

- [Complete virtual unwrapping and reading of a rolled Herculaneum papyrus](https://mlsanity.com/paper/2606.29085/) (2026)

## Abstract (arXiv)

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.