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arXiv · 2308.09370

TrOMR:Transformer-Based Polyphonic Optical Music Recognition

Yixuan Li·Huaping Liu·Qiang Jin·Miaomiao Cai·Peng Li
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Abstract

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.

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Yixuan Li, Huaping Liu, Qiang Jin, Miaomiao Cai, Peng Li. 2023-08-18. TrOMR:Transformer-Based Polyphonic Optical Music Recognition. https://arxiv.org/abs/2308.09370

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