Optical Music Recognition of Jazz Lead Sheets
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arXiv
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| Format: | Preprint |
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2025
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| author | Martinez-Sevilla, Juan Carlos Foscarin, Francesco Garcia-Iasci, Patricia Rizo, David Calvo-Zaragoza, Jorge Widmer, Gerhard |
| author_facet | Martinez-Sevilla, Juan Carlos Foscarin, Francesco Garcia-Iasci, Patricia Rizo, David Calvo-Zaragoza, Jorge Widmer, Gerhard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optical Music Recognition of Jazz Lead Sheets Martinez-Sevilla, Juan Carlos Foscarin, Francesco Garcia-Iasci, Patricia Rizo, David Calvo-Zaragoza, Jorge Widmer, Gerhard Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Optical Music Recognition of Jazz Lead Sheets |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05329 |