Update README.md

README.md

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----
-license: cc0-1.0
----
+---
+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
+---
+
+# Public-Domain 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
+
+## 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 Abdulina},
+  year = {2025},
+}