Automatic Scribe Attribution for Medieval Manuscripts. Issue 1 (24th December 2018)

• Record Type: Journal Article
• Authors: Dahllöf, Mats
• Is Part Of: Digital medievalist. Volume 11:Issue 1(2018)
• Publication Date: 2018-12-24
• Subjects: palaeography -- manuscript -- handwriting -- scribe -- scribe attribution -- letter -- classification -- digital palaeography -- digital humanities; Digital media -- Periodicals; Civilization, Medieval -- Study and teaching -- Periodicals; Middle Ages -- Study and teaching -- Periodicals; 909.07
• DOI: 10.16995/dm.67
• Languages: English
• ISSNs: 1715-0736

Abstract

Abstract : We propose an automatic method for attributing manuscript pages to scribes. The system uses digital images as published by libraries. The attribution process involves extracting from each query page approximately letter-size components. This is done by means of binarization (ink-background separation), connected component labelling, and further segmentation, guided by the estimated typical stroke width. Components are extracted in the same way from the pages of known scribal origin. This allows us to assign a scribe to each query component by means of nearest-neighbour classification. Distance (dissimilarity) between components is modelled by simple features capturing the distribution of ink in the bounding box defined by the component, together with Euclidean distance. The set of component-level scribe attributions, which typically includes hundreds of components for a page, is then used to predict the page scribe by means of a voting procedure. The scribe who receives the largest number of votes from the 120 strongest component attributions is proposed as its scribe. The scribe attribution process allows the argument behind an attribution to be visualized for a human reader. The writing components of the query page are exhibited along with the matching components of the known pages. This report is thus open to inspection and analysis using the methods and intuitions of traditional palaeography. The present system was evaluated on a data set covering 46 medieval scribes, writing in Carolingian minuscule, Bastarda, and a few other scripts. The system achieved a mean top-1 accuracy of 98.3% as regards the first scribe proposed for each page, when the labelled data comprised one randomly selected page from each scribe and nine unseen pages for each scribe were to be attributed in the validation procedure. The experiment was repeated 50 times to even out random variation effects.