The Herculaneum papyri, carbonised by the 79 AD eruption of Mount Vesuvius, can be imaged by X-ray micro-CT, yet their carbon-based ink is nearly iso-attenuating with the carbonised substrate and usually leaves no hight contrast in the reconstructed volume. State-of-the-art ink readers are supervised neural networks trained against labour-intensive labels and tuned on cropped regions of each segment; their letter-by-letter legibility is uneven and they offer little explainability. Motivated by the community’s call for a clearer understanding of the patterns underlying ink detection, we present a deterministic, training-free alternative that treats the unwrapped depth slab as a genuine 3-D signal. Three established texture descriptors — first-order statistics, Laws’ texture energy and block spatial autocorrelation — are lifted to volumetric form and evaluated both individually as stand-alone ink responses and jointly as a per-voxel mean fusion; the resulting volumetric response is projected to 2-D under a per-specimen depth prior, smoothed by a soft Markov random field and binarised at an adaptive per-specimen threshold. Across 25 manually annotated Greek-letter specimens of Scroll 5 (PHerc. 172), under a δ-pixel exclusion band around the ink-mask contour, the best single descriptor reaches a mean Matthews correlation coefficient (MCC) of 0.374, strictly positive on every specimen. Run on the full segment, the same
operator also surfaces partially recognisable text whose letters match the community ink-prediction of a neighbouring segment that overlaps with the analysed segment in volume. The pipeline is thus offered as a deterministic, explainable complementary signal that, in favourable cases, supervised neural detectors may consume as an additional input or weak label.
Publication Date: 2026-06-19