⚠️ Work in Progress! 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).
    • 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:

from datasets import load_dataset
from PIL import ImageDraw
import json


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

  Args:
      row (dict): A row from the dataset.
      image (PIL.Image): An image object without bounding boxes.

  Returns:
      PIL.Image: An image with bounding boxes drawn.
  """
  # Create a drawing object
  draw = ImageDraw.Draw(image)

  # Iterate through regions in the row
  for index, region in enumerate(json.loads(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"]

      # Drawing bounding box
      top_left = (box_x, box_y)
      bottom_right = (box_x + box_width, box_y + box_height)
      draw.rectangle([top_left, bottom_right], width=3, outline="red")

      # Show region data
      print(f"\nregion {index}"
            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['train']

  # Iterate through rows in the dataset
  for row in ds:
      # Load the image
      image = row["image"]

      # Draw bounding boxes on the image
      image = draw_bounding_boxes(row, image)

      # 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:

@preprint{MartinezSevillaPRAIG24,
  author = {Juan C. Martinez{-}Sevilla and
            Noelia Luna{-}Barahona and
            Joan Cerveto{-}Serrano and
            Antonio Rios{-}Vila and
            David Rizo and
            Jorge Calvo{-}Zaragoza},
  title = {A Multi{-}Texture Sheet Music Recognition Benchmark},
  year = {2024}
}