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--- |
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language: |
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- en |
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license: cc0-1.0 |
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task_categories: |
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- image-feature-extraction |
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tags: |
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- omr |
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- sheet-music |
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- music-notation |
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- public-domain |
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- benchmark |
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pretty_name: Muse OMR Benchmark |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Muse OMR Benchmark |
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## What this is |
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A small, clean benchmark dataset for **OMR (Optical Music Recognition — recognizing music notation from images/PDFs)**. |
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It contains **1077 pairs**: |
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- a symbolic music score (the “ground truth”, see dataset fields below) |
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- a corresponding **PDF** rendering with **data augmentation** applied |
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All underlying works are **Public Domain**. |
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## Why it exists |
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OMR is often evaluated on private or inconsistent datasets. This dataset aims to provide the community with a practical, reproducible, public benchmark. |
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## What’s inside |
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Each PDF is generated from our own catalog of PD scores and then augmented to simulate real-world scans: |
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- ink blobs / stains |
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- scratches / wear |
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- crumpled or textured paper |
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- rotation / skew |
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- other visual noise |
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## Benchmark Code |
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Check out official repo with evaluation code - https://github.com/musescore/omr_benchmark |
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## Dataset structure |
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The dataset is distributed as **pairs**. Typical fields: |
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- `id`: unique sample id |
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- `pdf_image`: augmented PDF file |
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- `score`: symbolic reference in MuseScore Studio file format for evaluation |
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## License |
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Dataset content is released under **CC0-1.0** (no restrictions; attribution appreciated). |
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## Citation |
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If you use this dataset in a paper or a public benchmark, please cite: |
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```bibtex |
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@dataset{pd_omr_benchmark, |
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title = {Muse OMR Benchmark}, |
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author = {Vasily Pereverzev and Kristina Abdullina}, |
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year = {2025}, |
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} |
