Optical Music Recognition of Jazz Lead Sheets
This work addresses a domain-specific problem for musicians and researchers by enabling automated recognition of jazz scores, but it is incremental as it builds on existing OMR methods for a new data type.
The paper tackles Optical Music Recognition for handwritten jazz lead sheets by introducing a novel dataset of 293 sheets with 2021 staves and developing a model that addresses chords and variability, releasing all resources publicly.
In this paper, we address the challenge of Optical Music Recognition (OMR) for handwritten jazz lead sheets, a widely used musical score type that encodes melody and chords. The task is challenging due to the presence of chords, a score component not handled by existing OMR systems, and the high variability and quality issues associated with handwritten images. Our contribution is two-fold. We present a novel dataset consisting of 293 handwritten jazz lead sheets of 163 unique pieces, amounting to 2021 total staves aligned with Humdrum **kern and MusicXML ground truth scores. We also supply synthetic score images generated from the ground truth. The second contribution is the development of an OMR model for jazz lead sheets. We discuss specific tokenisation choices related to our kind of data, and the advantages of using synthetic scores and pretrained models. We publicly release all code, data, and models.