## 1 Introduction  
The digitisation and encoding of historical music manuscripts is an ongoing area of research for many scientists. The aim is to preserve the vast amount of cultural heritage but also to provide musical information in a machine-readable form (e.g., \"*kern ([[http://www.humdrum.org/rep/kern/](http://www.humdrum.org/rep/kern/)]([http://www.humdrum.org/rep/kern/](http://www.humdrum.org/rep/kern/))), MEI ([[https://music-encoding.org/](https://music-encoding.org/)]([https://music-encoding.org/](https://music-encoding.org/))), or MusicXML [1]). For one thing, this enables music scientists to apply large-scale musical analysis such as detecting similarities of melodies, creating musical grammars, or comparing different versions of the same piece of music. Furthermore, the digital transcriptions are published in a collection [2] that is searchable and provides tools for data retrieving. The current _Corpus Monodicum_ project at the University of Wurzburg is dedicated to the exploration and edition of historically significant music, editorially untapped stocks of unanimous ecclesiastical and secular music of the European Middle Ages with Latin text. Two volumes [3; 4] have already been published; however, the majority of material of interest has not been converted into machine actionable form yet. Our proposed Optical Music Recognition (OMR) aims to speed up this process considerably. If in the process of encoding, the data maintains positional information of symbols or regions relative to original manuscript, ambiguities, corrections or other sources of manual interpretation can be looked up and verified in the original document. In general, this should highly improve the quality of research and reproducibility. Currently, the digital acquisition heavily relies on human effort because the ancient manuscripts particularly suffer from degradation and non-standardised fonts, glyphs or layouts. Therefore novel techniques in the area of artificial intelligence are required to capture the encoded data in a computer readable form. This paper focuses on the digitisation of medieval monophonic music written in square notation, an ancient notation which was developed and used from the 11 th-12 th century onwards [5]. Compared to even earlier forms, this writing of music is already similar to Common Western Music Notation in the sense that it uses four staff lines, clefs, accidentals and more or less discrete notes that are drawn as squares (compare Figure 1). However, unlike modern notation, notes are mostly connected to groups, the so-called neumes witch depict a small segment of melodic motion. Historically, neumes arise from single solid or broken stroke drawings that visually followed the pitch levels. Figure 1 also shows an actual transcription of the upper line taken from our data set in a modernised form where each note component (NC) is a single note. The graphical connectivity of two NCs, which is in most cases straightforward to decide, is marked by a slur (a). Example (b) shows two notes that are two single tone neumes and are therefore notated with a larger space. Logical connected NCs are visualised by a smaller space between two notes (c). This, however, is in some cases difficult to decide because there is no difference between a neume consisting of multiple single NCs or multiple neumes comprising one NC. In this example, the syllables each belonging to different neumes dissolve this ambiguity. The aim of this paper is, on the one hand, to detect the staff lines that form staves and on the other hand, to locate all musical symbols including the discrete note position of the neumes, so that the musical relevant information including the positions in the original document is fully recognised and preserved. Both tasks are conducted separately by usage of two Fully Convolutional Networks (FCNs), which predict pixel based locations of staff lines or music symbols, respectively. Further post-processing steps extract the actual positions and symbol types.