利用表面形貌差异，用机器学习从碳化卷轴中识别墨迹。
Ink Detection from Surface Topography of the Herculaneum Papyri
- 基于三维光学形貌数据训练模型，区分墨迹与纸张区域。
- 高分辨率形貌数据足以实现有效墨迹检测，分辨率下降则性能减弱。
- 为闭合卷轴的X射线形态学阅读提供分辨率参考目标。
解读赫库兰尼姆卷轴极为困难，因其卷轴与碳基墨水均已碳化。传统X射线成像依赖密度或成分对比，但碳墨在碳化纸上的衰减对比微弱。基于形貌假设，我们发现书写区域的表面形貌蕴含足够信号以区分墨迹与纸张。为此，我们在机械展开的赫库兰尼姆卷轴上，利用三维光学轮廓数据训练机器学习模型，分离墨迹与无墨区域。进一步量化了横向采样对可学习性的影响，并研究原分辨率模型在降采样输入下的表现。结果表明，仅高分辨率形貌数据即可提供可用信号用于墨迹检测。随着横向分辨率降低，分割性能下降，揭示了需解析的特征空间尺度。这些发现为通过X射线断层扫描进行闭合卷轴的形态学读取设定了空间分辨率目标。
原文摘要 · Abstract (English)
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.
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