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Digital scribe7/28/2023 ![]() ![]() ![]() The system has access to a set of writing examples which constitutes a database of known scribes.Ī secondary purpose of the present system is to produce arguments for scribe attributions which are comprehensible to a traditional palaeographer or even an ordinary human reader. In this context, each scribe (or we could say class) is identified as the hand behind a set of given manuscript images. This is essentially a classification problem. 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. To be more specific, it will focus on automatic scribe attribution. This article will be concerned with using computational resources to compare parts of manuscripts. A modern expert trying to place an odd medieval page in its context of production would most likely use a computer, at least for viewing digitized manuscripts. This gives us new opportunities to compare manuscripts and to find new connections among them. Today many libraries are in the process of digitizing their historical collections. Browsing thousands of 15th century manuscripts, one-by-one, library-by-library, to compare them with an enigmatic leaf, would necessitate enormous efforts, even if the most efficient of philologists would contribute their competence to the project. Just to see that two pages are from the same codex, scribe, or cultural context, and to justify such a conclusion, requires the expertise of a palaeographer. If we are interested in finding the manuscript home of an odd medieval fragment of a more ordinary kind, say a piece of parchment with Latin text written in some cursive script typical of the 15th century, we face a difficult problem. the Gothic language and alphabet, the extraordinary design, and the textual content, which strongly speak in favour of the conclusion that the solitary leaf belongs to the evangeliary. There are a number of circumstances, e.g. Philologists could later definitely verify that the Speyer leaf was one of those missing from the codex. 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. 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. In 1970, workers involved in the restoration of a chapel in Speyer found a reliquary containing a very old manuscript leaf. 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. The present system was evaluated on a data set covering 46 medieval scribes, writing in Carolingian minuscule, Bastarda, and a few other scripts. This report is thus open to inspection and analysis using the methods and intuitions of traditional palaeography. The writing components of the query page are exhibited along with the matching components of the known pages. The scribe attribution process allows the argument behind an attribution to be visualized for a human reader. The scribe who receives the largest number of votes from the 120 strongest component attributions is proposed as its scribe. 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. 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. 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. ![]() The system uses digital images as published by libraries. The attribution process involves extracting from each query page approximately letter-size components. We propose an automatic method for attributing manuscript pages to scribes. ![]()
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