⚠️ 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.

Dataset Details

• Image Format: PNG
• Annotations:
  • Segmentation: Bounding boxes
  • Music encoding (system-level and full-page): Humdrum **kern
• Use Cases:
  • Optical Music Recognition (OMR): system-level, full-page
  • Image Segmentation: music regions

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 Image, ImageDraw

ds = load_dataset("PRAIG/SMB")

# First image of the train split
data = ds["train"][0]
image = data["image"]

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

for reg in data["regions"]:
    value = reg["bbox"]

    # Calculate positions and dimensions
    box_x = value["x"] / 100 * data["original_width"]
    box_y = value["y"] / 100 * data["original_height"]
    box_width = value["width"] / 100 * data["original_width"]
    box_height = value["height"] / 100 * data["original_height"]

    # Calculate the corners of the box
    top_left = (box_x, box_y)
    top_right = (box_x + box_width, box_y)
    bottom_left = (box_x, box_y + box_height)
    bottom_right = (box_x + box_width, box_y + box_height)

    # Draw the box
    draw.rectangle([top_left, bottom_right], width=3)

# Save the image with boxes
image.save("image.png")

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}
}