## 3 Dataset  
As a dataset we used 49 pages of the manuscript \"Graduel de Nevers\" (accessible at the Bibliotheque nationale de France ([[https://gallica.bnf.fr/ark:/12148/btv1b8432301z](https://gallica.bnf.fr/ark:/12148/btv1b8432301z)]([https://gallica.bnf.fr/ark:/12148/btv1b8432301z](https://gallica.bnf.fr/ark:/12148/btv1b8432301z)))) published in the 12 th Century. The handwritten music comprises different neume notation styles, only a part is written in square notation, which are folios 2-9 and 246-263. These pages were extracted and further split into three parts that share a similar layout or difficulty based on our human intuition (compare Figure 2). The first part contained pages with the best available notation quality with fading bleeding. Its staff lines are mostly straight, possibly due to usage of a ruler, and all neumes were very clear and distinct. The second part suffered from bleeding staff lines and was written very narrowly, yielding the most difficult notation. The third part comprised to some extent very unclear neumes and very wavy staff lines. In our experiments, we used these parts to estimate how well our trained algorithms generalise onto unseen layouts by not using all parts for training. The GT was manually annotated under the supervision of music scientists. For each stave, we stored four staff lines each as a polyline whose coordinates were relative to the the image. The exact start and end of a line was ambiguous due to occasional severe degradation. We further defined the symbols clef, accidental, and neume as part of a staff. A clef consists of a type (C or F), its centre as absolute position on the image and its location relative to the staff lines, which also marks the denoted staff line. In the example line in Figure 1 the F-clef is located on the second line. Since all occurring accidentals are a flat B, it is sufficient to store their absolute centre position and their staff line location. Each neume is comprised of single NCs. For each NC, we took its absolute position, its location on the staff lines, and whether it is graphically connected to the previous NC. For example the first neume in Figure 1 consists of two NCs. The first one is located on the first staff line while the second one is graphically connected and is positioned in the second space. The neume (c) in the example line comprises three NCs of which none is graphically connected; however, since they belong to the same neume, their logical connection can be derived. Note that, in general, the symbol location in a staff line can be computed using the positions of the staff lines and the global position of the symbol, however there exist many ambiguities as to whether a NC is actually located on a line or in a space. For instance, the first NC of the second last neume in Figure 1 might also be an G instead of an F. In this work, we did not distinguish additional note types such as liquecents or oriscus. Furthermore, unlike the approach in Reference [28], we did not label the images on a pixel-basis, since detecting the actual shape and extent of neumes, clefs, or accidentals is out of the focus of this paper. We were solely interested in transcribing historical neume notations in a digitised form that preserved all melodic information, which is the desired output in almost all cases. Using the described storage scheme, all required information for training and evaluation is preset or can be computed. Table 1 gives an overview of each part's properties by listing its number of pages, the number of staves on these pages, each consisting of four staff lines, and the symbol counts. In total, we annotated more than 16,000 symbols located in more than 500 staves. Compared to the number of symbols, the 65 flat accidentals occur very rarely, which makes the learning of their detection very challenging. Approximately 4% of the symbols are clefs, yet at least up to 600 clefs can be used as training examples. Figure 2: Each page represents parts 1, 2, and 3, respectively.