Two Journeys: Insights on the Annotation of Large-Scale Optical Music Recognition Datasets

The use of Optical Music Recognition (OMR) requires annotated music scores (namely, ground truth) for training or adaptation. Besides the level of structured encoding, a significant part of the existing OMR methods still needs ground truth also at image level, which is indeed still important for tasks adjacent to OMR. However, annotating images is a major part of OMR project costs: not just the work of the annotators themselves but also associated software development and process management. Unlike most other more prevalent image annotation tasks in industry, in the case of music notation, infrastructure is lacking, and many pitfalls exist. Thus, in this paper, we report on the combined experience of two ongoing OMR projects with major data collection and annotation efforts, and we formulate recommendations for managing music notation annotation projects, leading to efficient use of the inevitably limited resources.