Digital approaches to the capture and analysis of watermarks using the manuscripts of Isaac Newton as a test case

本项目将以艾萨克·牛顿（Isaac Newton）的分散手稿语料库为测试案例，研究数字人文学术中普遍应用的两个研究领域。测试案例的直接目的是使用人工智能来帮助识别和分类牛顿材料中的水印，并在此过程中建立一个通用工具来帮助组织和确定手稿的日期。这个项目还有更广泛的意义。该项目的第一阶段将是对适合自动分析的水印图像的生产技术进行方法学调查，使用新摄影和探索现有图像中的潜在潜力。在第二阶段，我们将开发计算机视觉方法来系统地聚类和匹配手稿和收藏中的水印图像组装语料库。通过该项目开发的方法将可转移到牛顿语料库以外的水印收藏中，为寻求通过水印匹配分析、确定日期和组织历史收藏的学者创造一种方法，为寻求在对水印文件进行成像和数字化时建立标准化测量和记录方法的保护人员创造一种方法。该项目的最后阶段将使我们能够通过研究研讨会、网络工具和对在线数据库的改进以及期刊上的传统出版物来传播我们的发现。自从查尔斯Moïse布里凯（Charles Briquet） 20世纪早期开创性的研究以来，水印已经成为确定日期的手稿的中心部分。布里凯在1907年出版的具有里程碑意义的目录《细丝》（Les filigranes），使得研究人员通过参考布里凯目录中的范例，在手稿中发现的1600年前的水印，原则上有可能追溯到现在（在某种程度上是本地化的）。由于伯恩斯坦财团（https://memoryofpaper）的支持，本目录和其他目录已被数字化。Eu /)，研究和技术的进步揭示了传统方法的局限性，传统方法需要耗时的程序和一定程度的专门知识来识别每个水印。要在现有水印和任何目录中复制的水印之间找到精确的匹配是非常困难的，首先是因为目录的全面性有限，其次是因为每个水印都有两个“孪生”版本，不可能完全相同，并且由于在造纸过程中反复使用，随着时间的推移会变形。通过发展和加强新的方法和技术来改进水印的获取和分析，我们希望解决基本问题，从而为所有必须依赖纸质文件作为时间证据的人提供好处。由于机器学习和人工智能，计算机视觉近年来取得了重大进展，该项目将建立在法国国立夏尔学院及其合作伙伴（特别是École des Ponts ParisTech的计算机科学家）已经开展的前沿工作的基础上，研究图像匹配问题，特别是水印，跨格式（照片和跟踪）。在创建用于训练和开发由夏尔学院创建的开源软件的图像语料库时，我们将以国家档案馆（TNA）最近的工作为基础，使用相对负担得起的设备和技术来生成高度适合机器分析的水印图像。该项目将开发和应用这两种方法，以尝试增强计算机视觉软件，使其能够解锁在反射光下拍摄的数千张现有图像中所包含的潜在信息，这些图像已被各机构数字化并通过IIIF提供。

This project will investigate two research areas with general application in digital humanities scholarship, using the dispersed manuscript corpus of Isaac Newton as a test case. The immediate purpose of the test case will be to use artificial intelligence to assist with the identification and classification of watermarks in Newton material and, in the process, to build a general tool to assist with the organisation and dating of manuscripts. The project also has much wider significance. The project's first stage will be the methodological investigation of techniques for the production of images of watermarks which are suitable for automated analysis, using both new photography and the exploration of the potential latent in existing images. During the second stage, we will develop computer vision methods to systematically cluster and match the assembled corpus of watermark images across manuscripts and collections. Methods developed through this project will be transferrable to watermark collections beyond that of Newton's corpus, creating a methodology for scholars seeking to analyse, date, and organise historical collections via watermark matching, and for conservators seeking to establish standardised surveying and documentation methods while imaging and digitising watermarked documents. A final stage of the project will allow us to disseminate our findings through research workshops, web tools, and improvements to online databases, as well as traditional publications in journals.Since the groundbreaking early twentieth-century research of Charles Moïse Briquet, watermarks have formed a central part in the dating of otherwise undated manuscripts. Briquet's monumental 1907 catalogue, Les filigranes, made it possible, in principle, to date (and to some extent localise) pre-1600 watermarks found by researchers in manuscripts by reference to exemplars in Briquet's catalogue. While this catalogue and others have been digitised thanks to the Bernstein consortium (https://memoryofpaper.eu/), advances in research and technology have revealed the limitations of the traditional approach, which requires time-consuming procedures and some degree of expertise for the identification of each single watermark. It is very difficult to find exact matches between watermarks in situ and those reproduced in any catalogue, first due to the limited comprehensiveness of the catalogues, and, second, because each individual watermark is produced in two "twin" versions, never perfectly identical, and suffers deformation over time as a result of repeated use in the paper manufacturing process. By developing and enhancing new approaches and techniques to improve the acquisition and analysis of watermarks, we hope to solve basic problems and thereby provide benefit to all who must rely upon paper documents for chronological evidence. While computer vision has made significant progress in recent years thanks to machine learning and artificial intelligence, this project will build on cutting-edge work already undertaken by the Ecole Nationale des Chartes and its partners (notably the computer scientists at École des Ponts ParisTech) to investigate the problem of matching images, specifically of watermarks, across formats (photographs and tracings). In creating a corpus of images used to train and develop the open source software created by the Ecole des Chartes we will build on recent work by The National Archives (TNA) to use comparatively affordable equipment and techniques to produce images of watermarks that are highly suitable for machine analysis. The project will develop and apply both of these approaches in order to attempt to enhance the computer-vision software so that it may be able to unlock the latent information held in thousands of existing images shot in reflected light which institutions have already digitised and made accessible through IIIF.