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

Lead Research Organisation: University of Cambridge
Department Name: History

Abstract

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.

Publications

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