Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of paper stock and gives information about chronology, localisation and movement of books and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation for the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables the feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale.

翻译：中世纪手工纸由模具制成，模具会在纸张上留下不可磨灭的印记。这些印记包括帘条纹、筛网纹和水印，通常在纸面清晰可见。提取这些特征可实现纸料鉴定，并获知书籍与人员的年代、地域分布及流动信息。目前纸张分析的特征提取计算研究大多聚焦于X光摄影或透射光图像。虽然这些成像方法能清晰呈现目标特征，但其采集成本高、耗时长，中小型机构难以采用。相比之下，中世纪手稿纸的反光图像资源丰富且采集成本较低。本文提出从反光图像中检测并提取筛网纹与帘条纹的算法。我们利用谱全变差分解方法解决反光图像的主要缺陷——因噪声和退化导致的线条低对比度衰减与强度跳变，并开发后续线条提取方法。研究结果清晰证明了反光图像在纸张分析中的可行性。这项工作使得此前因缺乏适当图像而无法分析的纸质手稿得以实现特征提取，同时为大规模纸料鉴定开辟了新路径。