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.
Highlights
Using the components extracted from the labelled manuscript images, the system predicts a scribe for each component extracted from a query page or spread by means of “nearest neighbour” classification
As studies in medieval scribe attribution are few, and the data sets used in evaluations have had different properties, it is not possible to make a fully-fledged comparison of the present system with previous ones, as regards their performance as classifiers in scribe attribution
We have outlined and evaluated an automatic system for identifying the most plausible scribe responsible for the writing found in a manuscript image
Summary
In 1970, workers involved in the restoration of a chapel in Speyer found a reliquary containing a very old manuscript leaf. Experts called in to examine the item were— we can imagine—excited to find writing in gold and silver ink on purple vellum using a somewhat odd alphabet These details must soon have led their inquiries in the direction of the evangeliary known as Codex Argenteus, which, after dramatic travels, had ended up in Uppsala. The main purpose of the system described here is to predict, by means of automatic analysis of digital images, which scribe has produced the writing on a manuscript sample. In connection with this study, we have compiled and published open-source a data set comprising 46 medieval scribes writing in book hand scripts (see Appendix for details)
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