README.md

---
license: other
license_name: see-praig-upstream
task_categories:
  - image-to-text
tags:
  - omr
  - optical-music-recognition
  - kern
  - humdrum
  - musicxml
  - music
  - historical-scans
  - polish
  - benchmark
  - evaluation
size_categories:
  - n<1K
pretty_name: Polish Historical-Scan OMR Benchmark
---

# Polish Historical-Scan OMR Benchmark

A page-level Optical Music Recognition (OMR) evaluation benchmark of 112 real
historical score scans, paired with both `**kern` (Humdrum) and MusicXML
ground-truth transcriptions.

Derived from the [`PRAIG/polish-scores`](https://huggingface.co/datasets/PRAIG/polish-scores)
dataset, with kern normalization and a manual-fix pass applied. Released as
the **real-scan** half of the Transcoda evaluation suite alongside
[`btrkeks/verovio-synth-omr`](https://huggingface.co/datasets/btrkeks/verovio-synth-omr)
and the [`btrkeks/transcoda-59M-zeroshot-v1`](https://huggingface.co/btrkeks/transcoda-59M-zeroshot-v1)
model.

## Intended Use

> **Evaluation only. Do not include in training data.**

In the Transcoda paper and benchmarks, no Polish samples are used for model
training or selection — this dataset exists exclusively to measure
out-of-distribution OMR performance on real historical scans.

## Dataset Structure

A single unsplit table.

| Column | Type | Description |
| --- | --- | --- |
| `image` | `Image` (RGB) | Scan of a Polish historical score page, resized to **1485 × 1050** pixels (width × height). |
| `transcription_kern` | `string` | Ground-truth `**kern` (Humdrum) transcription. |
| `transcription_xml` | `string` | Ground-truth MusicXML transcription of the same content. |

Image contract: each scan is resized to width 1050 preserving aspect ratio,
then bottom-padded with white or top-cropped to height 1485.

## Provenance

1. Upstream: [`PRAIG/polish-scores`](https://huggingface.co/datasets/PRAIG/polish-scores)
   (curated by the Pattern Recognition and Artificial Intelligence Group,
   University of Alicante).
2. Raw `.ekern` transcriptions extracted from the upstream dataset.
3. Converted to standard `**kern` by stripping the extra `.ekern` annotation
   symbols.
4. Multi-pass normalization (spine cleanup, interpretation spacing repair,
   rest folding, etc.).
5. **Manual curation pass** to fix transcription errors discovered during
   benchmark validation.
6. Images resized to the 1485 × 1050 page contract.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("btrkeks/polish-scores")
sample = ds["train"][0]
sample["image"]                 # PIL.Image, 1050 × 1485
sample["transcription_kern"]    # str (**kern)
sample["transcription_xml"]     # str (MusicXML)
```

## Benchmark Numbers

OMR-NED on this dataset (lower is better):

| Model | Params | OMR-NED ↓ |
| --- | ---: | ---: |
| SMT++ | 11M | 80.16% |
| Legato | 943M | 86.73% |
| Transcoda 59M (beam search) | 59M | 63.97% |
| Transcoda FCMAE + ConvNeXt-V2-Base | 120M | **60.7%** |

See `scripts/benchmark/README.md` in the project repository for reproduction
commands and full ablations.

## License

The upstream `PRAIG/polish-scores` dataset does not declare a license at the
time of this writing. This derivative is therefore released under
`license: other` until upstream terms are clarified.

If you intend to redistribute or use this data commercially, please contact
PRAIG (University of Alicante) directly about the original scans, and treat
this card's `other` license as a placeholder rather than a grant.

## Limitations

- Small (112 samples) — high per-sample variance in metrics; use as a
  qualitative OOD probe, not a high-precision leaderboard.
- Historical scans include genuine degradation (paper aging, ink bleed, faint
  staves, hand-written annotations).
- Manual curation was applied; transcriptions reflect the curator's reading,
  not necessarily the upstream PRAIG ground truth.

## Citation

Please cite both the upstream PRAIG dataset and the Transcoda paper when
using this benchmark:

```bibtex
@misc{dratschuk2026transcodaendtoendzeroshotoptical,
      title={Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training},
      author={Daniel Dratschuk and Paul Swoboda},
      year={2026},
      eprint={2605.10835},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.10835},
}

@misc{praig_polish_scores,
  title  = {Polish Scores Dataset},
  author = {Pattern Recognition and Artificial Intelligence Group (PRAIG)},
  howpublished = {\url{https://huggingface.co/datasets/PRAIG/polish-scores}}
}
```