Optical Music Recognition (OMR) has seen major progress in model design, with end-to-end methods now capable of recognising notation at all levels of complexity. However, the impact of this progress has been limited by the visual domains of available training datasets, which are largely born-digital. Existing large collections of sheet music in libraries and other heritage institutions contain predominantly manuscripts, whose visual domains are highly diverse and different, so existing OMR systems fail when applied in the real world. These institutions are often resource-constrained, so large in-domain datasets cannot be expected. We provide a first baseline on real-world manuscripts with complex piano notation in the resource-constrained scenario. Using fine-grained music notation graph (MuNG) annotations and the Smashcima synthesis tool, we then show that while some direct transcriptions of in-domain data remain essential, domain adaptation using synthetic musical manuscript images brings significant improvement. Furthermore, the symbols used do not need to be in-domain, so the expensive fine-grained annotation can be avoided. We thus bring OMR closer to one of its stated goals: preserving and promoting musical cultural heritage.

翻译：光学音乐识别（OMR）在模型设计方面取得了重大进展，端到端方法现已能够识别所有复杂程度的乐谱符号。然而，这一进展的影响受到可获取训练数据集视觉领域的限制——现有数据集主要源自数字生成。图书馆及其他文化遗产机构中现存的大量乐谱集以手稿为主，其视觉领域高度多样且迥异，导致现有OMR系统在真实世界应用中失效。这些机构通常资源受限，因此难以构建大规模领域内数据集。我们针对资源受限场景下包含复杂钢琴谱记的真实世界手稿，提供了首个基准。通过利用细粒度音乐符号图（MuNG）标注和Smashcima合成工具，我们证明尽管部分领域内数据的直接转录仍然不可或缺，但基于合成音乐手稿图像的领域适应能带来显著性能提升。此外，所用符号无需局限于领域内数据，从而可避免成本高昂的细粒度标注。由此，我们使OMR更接近其既定目标之一：保护与推广音乐文化遗产。