TrOMR:Transformer-Based Polyphonic Optical Music Recognition
Yixuan Li Huaping Liu Qiang Jin Miaomiao Cai Peng Li
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.