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.

Regions

• Type: List of JSON objects
• Description: Contains detailed information about regions on the page. Each JSON object includes:
  • bbox: Axis-aligned bounding box. All values are expressed as percentages (0–100) relative to the image dimensions.
    • x: Horizontal position of the left edge (percentage of image width).
    • y: Vertical position of the top edge (percentage of image height).
    • width: Width of the bounding box (percentage of image width).
    • height: Height of the bounding box (percentage of image height).
  • 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.

SMB usage 📖

SMB is publicly available at HuggingFace.

To download from HuggingFace:

1. Gain access to the dataset and get your HF access token from: 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 for details.
3. Use the following code to load SMB and extract the regions:

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.
  """
  image = row["image"]
  draw = ImageDraw.Draw(image)

  for index, region in enumerate(row["regions"]):
      bbox = region["bbox"]
      x = bbox["x"] / 100 * row["original_width"]
      y = bbox["y"] / 100 * row["original_height"]
      w = bbox["width"] / 100 * row["original_width"]
      h = bbox["height"] / 100 * row["original_height"]

      draw.rectangle([x, y, x + w, y + h], outline="red", width=3)

      print(f"\nRegion {index}:\nkern: {region['kern']}")

  return image


if __name__ == "__main__":
  ds = load_dataset("PRAIG/SMB")
  ds = ds["test"]

  for row in ds:
      image = draw_bounding_boxes(row)
      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):

@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},
}