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.

翻译：阅读赫库兰尼姆纸莎草纸的挑战在于，卷轴本身和基于碳的墨迹均经历了碳化。在X射线放射学和断层成像中，墨迹检测通常依赖于密度或成分产生的对比度，但碳化纸莎草纸上的碳基墨迹几乎不提供衰减对比。基于形态学假设，我们证明书写区域的表面形态包含足够信号以区分墨迹与纸莎草纸。为此，我们训练机器学习模型，利用机械展开的赫库兰尼姆纸莎草纸的三维光学轮廓测量数据来分离有墨迹与无墨迹区域。我们进一步量化横向采样如何决定可学习性，以及原生分辨率模型在粗化输入上的表现。研究表明，仅凭高分辨率地形学数据便包含可用于墨迹检测的信号。随横向分辨率降低而下降的分割性能，揭示了必须在我们数据集中解析的、利用形态学信号所需的空间尺度特征。这些发现为通过X射线断层成像基于形态学读取封闭卷轴所需的空间分辨率目标提供了依据。