Optical Music Recognition (OMR) is an important technology in music and has
been researched for a long time. Previous approaches for OMR are usually based
on CNN for image understanding and RNN for music symbol classification. In this
paper, we propose a transformer-based approach with excellent global perceptual
capability for end-to-end polyphonic OMR, called TrOMR. We also introduce a
novel consistency loss function and a reasonable approach for data annotation
to improve recognition accuracy for complex music scores. Extensive experiments
demonstrate that TrOMR outperforms current OMR methods, especially in
real-world scenarios. We also develop a TrOMR system and build a camera scene
dataset for full-page music scores in real-world. The code and datasets will be
made available for reproducibility.

该研究提出了一种基于 Transformer 的方法，名为 TrOMR，具有优秀的全局感知能力，用于端到端的复调 OMR。通过引入一种新颖的一致性损失函数和合理的数据注释方法来提高对复杂乐谱的识别精度，并在大量实验证明 TrOMR 在真实场景中优于当前 OMR 方法。同时，研究开发了一个 TrOMR 系统，并构建了一个用于真实世界中的全页乐谱的相机场景数据集。提供了代码和数据集以便于重现。