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Datasets:
PRAIG
/
SMB

Tasks:
Image-to-Text
Image Segmentation
Text Retrieval
Modalities:
Image
Text
Formats:
imagefolder
Size:
< 1K
Tags:
music
documents
end-to-end
full-page
system-level
Libraries:
Datasets
License:
Dataset card Data Studio Files
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SMB / README.md
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metadata 
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

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.

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:
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):

@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},
}