Papers › LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR
LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR
Guang Yang, Victoria Ebert, Nazif Tamer, Luiza Pozzobon, Noah A. Smith
We propose Legato, a new end-to-end transformer model for optical music recognition (OMR). Legato is the first large-scale pretrained OMR model capable of recognizing full-page or multi-page typeset music scores and the first to generate documents in ABC notation, a concise, human-readable format for symbolic music. Bringing together a pretrained vision encoder with an ABC decoder trained on a dataset of more than 214K images, our model exhibits the strong ability to generalize across various typeset scores. We conduct experiments on a range of datasets and demonstrate that our model achieves state-of-the-art performance. Given the lack of a standardized evaluation for end-to-end OMR, we comprehensively compare our model against the previous state of the art using a diverse set of metrics.
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