# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 3 Related Work  
In the literature, optical music recognition (OMR) is employed quite broadly, with definitions varying depending on the dataset or task at hand. [PERSON] et al. (2020) (2020) defined OMR as \"a field of research that investigates how to computationally read music notation in documents\", primarily focusing on the musical aspect. However, this definition is insufficient for our purposes and must be expanded, as our dataset comprises church chants where not only the musical notation but also the lyrics, syllable alignment with symbols, and extraction of different chants within one or multiple pages are relevant and require transcription. Therefore, we divide our pipeline into two main steps: First, the extraction of musical notation as defined in (2020), and second, the extraction of text, syllable assignment to notes, and extraction of individual chants. Unfortunately, comparing such a pipeline is challenging due to the absence of comparable complete transcription processes in the literature that have been depicted and evaluated. Additionally, there is a scarcity of related work on OMR. [PERSON] (2020) (2020) noted approximately 500 published works in this field. However, when considering specific OMR  
Figure 1: Elements of a document that have to be extracted. The following elements need to be extracted and examples are highlighted in the image: Music–lyric region pairs, note components (start, gapped, i.e., connected, looped, i.e., graphically connected), clefs (C-clef, F-clef), accidentals (sharp, flat, natural), drop capitals (orange marked), individual chants (blue marked), lyrics and the division into syllables (red vertical lines), and the assignment of syllables to note sequences (purple arrow). The modern transcription of the highlighted chant is shown in Section 5.7.  
steps, notations, modern versus historical data, or monophonic versus polyphonic music, the number of works available for comparison is significantly less. Especially historical data increases the complexity of comparing OMR algorithms since the data at hand can be quite heterogeneous in layout and difficulty. The majority of the existing OMR systems are designed for modern music notation, which is typically well preserved and homogeneous in layout and notation style. While existing end-to-end solutions for optical music recognition primarily emphasize symbol recognition and are tailored for modern documents featuring fixed clefs, they often overlook historical documents with varying staff lines or clefs that can significantly impact the melody. In our context, end-to-end refers to a system that produces the required outcome directly, with no intermediary results. Conversely, multi-stage systems involve multiple processing phases (steps) in a pipeline, whereby intermediate outcomes of one step are used in other steps. End-to-end systems for historical documents are rare and use assumptions like requiring image excerpts of individual staff systems [6], necessitating an additional layout processing step. Additionally, end-to-end segmentation-free pipelines have been developed that operate effectively with historical data, without necessitating any preprocessing steps (e.g., localization of individual staffline segments). However, at present, these single-stage systems are outperformed by multi-stage counterparts [7] and typically only extract the melody. Consequently, further steps would be necessary to extract lyrics and syllable note assignments. This can prove challenging, as current end-to-end pipelines are segmentation-free and therefore do not extract information such as position on the page, which may be necessary for lyric-note assignments. The Music Score Searching and Analysis (SIMSSA) project [8; 9] offers a workflow aimed at transcribing historical documents with chant music, employing a multi-stage approach similar to ours. However, the algorithms and approaches used differ significantly from ours.