README.md

---
arxiv: arxiv.org/abs/2506.10488
license: cc-by-nc-4.0
tags:
- music
- documents
- end-to-end
- full-page
- system-level
annotations_creators:
- manually expert-generated
pretty_name: Sheet Music Benchmark
size_categories:
- 1K<n<10K
task_categories:
- image-to-text
- image-segmentation
- text-retrieval
subtasks:
- document-retrieval
extra_gated_fields:
  Affiliation: text
configs:
- config_name: default
  data_files:
  - split: test
    path:
    - "test/**/*.png"
    - "metadata.jsonl"
---


# SMB: A Multi-Texture Sheet Music Recognition Benchmark 

## Overview

SMB (Sheet Music Benchmark) is a dataset of printed Common Western Modern Notation scores developed at the University of Alicante at the Pattern Recognition and Artificial Intelligence Group.

## Use Cases:
- Optical Music Recognition (OMR): system-level, full-page
- Image Segmentation: music regions

## Requesting access
As sometimes 🤗 is not emailing me when someone requests access. If you are interested, please reach out via email.

## Dataset Details

Each page includes the corresponding \**kern data for that specific page. Additionally, it provides detailed annotations for each region within the page.

### 1. Image
- **Type**: PNG
- **Description**: Encoded full-page image of the score.

### 2. Original Width
- **Type**: Integer
- **Description**: The width of the image in pixels.

### 3. Original Height
- **Type**: Integer
- **Description**: The height of the image in pixels.

### 4. Regions
- **Type**: List of JSON objects
- **Description**: Contains detailed information about regions on the page. Each JSON object includes:
  - **bbox**:
    - **x**: The vertical position on the page (in pixels).
    - **y**: The horizontal position on the page (in pixels).
    - **width**: Width of the region (in pixels).
    - **height**: Height of the region (in pixels).
    - **rotation**: Angle of rotation (in degrees) for the bounding box around its top-left corner. This angle defines how much the box is rotated clockwise from its default unrotated position.
  - **raw**: The content extracted from the original dataset before any processing.
  - **kern**: A standardized version of the content ready for rendering.
  - **ekern**: A tokenized and standardized version of the content for enhanced processing.

### 5. Page Texture
- **Type**: String
- **Description**: The musical texture of the page.
- **Values**: 
  - "Pianoform"
  - "Monophonic"
  - "Other"

### 6. Page
- **Type**: JSON object
- **Description**: Metadata of the page. Fields include:
  - **raw**: The unprocessed content extracted from the original dataset.
  - **kern**: The content in a standardized format, ready to be rendered.
  - **ekern**: The content in a tokenized and standardized format.

### 7. Score ID
- **Type**: String
- **Description**: Unique identifier for the original score to which the page belongs.


## SMB usage 📖
SMB is publicly available at [HuggingFace](https://huggingface.co/datasets/PRAIG/SMB).

To download from HuggingFace:

  1.  Gain access to the dataset and get your HF access token from: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
  2.  Install dependencies and login HF:
       - Install Python
       - Run `pip install pillow datasets huggingface_hub[cli]`
       - Login by `huggingface-cli login` and paste the HF access token. Check [here](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login) for details.
  3.  Use the following code to load SMB and extract the regions:


  ```python
import math
from datasets import load_dataset
from PIL import ImageDraw


def draw_bounding_boxes(row):
    """
    Draws bounding boxes on an image based on region data provided in the row.

    Args:
        row (dict): A row from the dataset.
    Returns:
        PIL.Image: An image with bounding boxes drawn.
    """
    # Load the image
    image = row["image"]

    # Create a drawing context
    draw = ImageDraw.Draw(image)

    # Iterate through regions in the row
    for index, region in enumerate(row["regions"]):
        # Extract bounding box data
        bbox = region["bbox"]
        box_x = bbox["x"] / 100 * row["original_width"]
        box_y = bbox["y"] / 100 * row["original_height"]
        box_width = bbox["width"] / 100 * row["original_width"]
        box_height = bbox["height"] / 100 * row["original_height"]
        rotation = bbox["rotation"]

        # Convert rotation to radians
        rotation_rad = math.radians(rotation)

        # Calculate the corners relative to the top-left corner (anchor point)
        corners = [
            (0, 0),                     # Top-left
            (box_width, 0),             # Top-right
            (box_width, box_height),    # Bottom-right
            (0, box_height),            # Bottom-left
        ]

        # Apply rotation around the top-left corner
        rotated_corners = []
        for x, y in corners:
            rotated_x = box_x + x * math.cos(rotation_rad) - y * math.sin(rotation_rad)
            rotated_y = box_y + x * math.sin(rotation_rad) + y * math.cos(rotation_rad)
            rotated_corners.append((rotated_x, rotated_y))

        # Draw the rotated rectangle
        draw.polygon(rotated_corners, outline="red", width=3)

        # Show region data
        print(f"\nRegion {index}:"
              f"\nRotation (degrees): {rotation}"
              f"\nkern: {region['kern']}")

    return image


if __name__ == "__main__":
    # Load dataset from Hugging Face
    ds = load_dataset("PRAIG/SMB")

    # Select a subset of the dataset
    ds = ds["test"]

    # Iterate through rows in the dataset
    for row in ds:
        # Draw bounding boxes on the image
        image = draw_bounding_boxes(row)

        # Show the image and wait for user to close it
        image.show()
        input("Close the image window and press Enter to continue...")

  ```

## Citation

If you use our work, please cite us (there is an arXiv version, but this one is the official):

```bibtex
@inproceedings{juan_c_martinez_sevilla_2025_17811446,
  author       = {Juan C. Martinez-Sevilla and
                  Joan Cerveto-Serrano and
                  Noelia Luna-Barahona and
                  Greg Chapman and
                  Craig Sapp and
                  David Rizo and
                  Jorge Calvo-Zaragoza},
  title        = {Sheet Music Benchmark: Standardized Optical Music
                   Recognition Evaluation
                  },
  booktitle    = {Proceedings of the 26th International Society for
                   Music Information Retrieval Conference
                  },
  year         = 2025,
  pages        = {618-625},
  publisher    = {ISMIR},
  month        = sep,
  venue        = {Daejeon, South Korea and Online},
  doi          = {10.5281/zenodo.17811446},
  url          = {https://doi.org/10.5281/zenodo.17811446},
}
```