The main challenges of Optical Music Recognition (OMR) come from the nature of written music, its complexity and the difficulty of finding an appropriate data representation. This paper provides a first look at DoReMi, an OMR dataset that addresses these challenges, and a baseline object detection model to assess its utility. Researchers often approach OMR following a set of small stages, given that existing data often do not satisfy broader research. We examine the possibility of changing this tendency by presenting more metadata. Our approach complements existing research; hence DoReMi allows harmonisation with two existing datasets, DeepScores and MUSCIMA++. DoReMi was generated using a music notation software and includes over 6400 printed sheet music images with accompanying metadata useful in OMR research. Our dataset provides OMR metadata, MIDI, MEI, MusicXML and PNG files, each aiding a different stage of OMR. We obtain 64% mean average precision (mAP) in object detection using half of the data. Further work includes re-iterating through the creation process to satisfy custom OMR models. While we do not assume to have solved the main challenges in OMR, this dataset opens a new course of discussions that would ultimately aid that goal.

中文翻译：

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DoReMi：第一眼看一下通用 OMR 数据集

光学音乐识别 (OMR) 的主要挑战来自书面音乐的性质、其复杂性以及找到合适的数据表示的难度。本文首先介绍了 DoReMi，这是一个解决这些挑战的 OMR 数据集，以及一个用于评估其效用的基线对象检测模型。鉴于现有数据通常不能满足更广泛的研究，研究人员通常会按照一组小阶段来处理 OMR。我们研究了通过提供更多元数据来改变这种趋势的可能性。我们的方法补充了现有的研究；因此 DoReMi 允许与两个现有数据集 DeepScores 和 MUSCIMA++ 协调。DoReMi 是使用乐谱软件生成的，包括 6400 多张打印的乐谱图像，并附有可用于 OMR 研究的元数据。我们的数据集提供了 OMR 元数据，MIDI、MEI、MusicXML 和 PNG 文件，每个文件都有助于 OMR 的不同阶段。我们使用一半的数据在目标检测中获得了 64% 的平均精度 (mAP)。进一步的工作包括重复创建过程以满足自定义 OMR 模型。虽然我们不认为已经解决了 OMR 中的主要挑战，但该数据集开启了一个新的讨论过程，最终将有助于实现这一目标。