---
language:
- en
license: cc0-1.0
task_categories:
- image-feature-extraction
tags:
- omr
- sheet-music
- music-notation
- public-domain
- benchmark
pretty_name: Muse OMR Benchmark
size_categories:
- 1K<n<10K
---

# Muse OMR Benchmark

## What this is
A small, clean benchmark dataset for **OMR (Optical Music Recognition — recognizing music notation from images/PDFs)**.

It contains **1077 pairs**:
- a symbolic music score (the “ground truth”, see dataset fields below)
- a corresponding **PDF** rendering with **data augmentation** applied

All underlying works are **Public Domain**.

## Why it exists
OMR is often evaluated on private or inconsistent datasets. This dataset aims to provide the community with a practical, reproducible, public benchmark.

## What’s inside

Each PDF is generated from our own catalog of PD scores and then augmented to simulate real-world scans:
- ink blobs / stains
- scratches / wear
- crumpled or textured paper
- rotation / skew
- other visual noise

## Benchmark Code
Check out official repo with evaluation code - https://github.com/musescore/omr_benchmark

## Dataset structure
The dataset is distributed as **pairs**. Typical fields:

- `id`: unique sample id
- `pdf_image`: augmented PDF file
- `score`: symbolic reference in MuseScore Studio file format for evaluation

## License
Dataset content is released under **CC0-1.0** (no restrictions; attribution appreciated).

## Citation
If you use this dataset in a paper or a public benchmark, please cite:

```bibtex
@dataset{pd_omr_benchmark,
  title = {Muse OMR Benchmark},
  author = {Vasily Pereverzev and Kristina Abdullina},
  year = {2025},
}