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Optical Music Recognition in Manuscripts from the Ricordi Archive

Important

If you like our work and use it in your work, cite us:

Simonetta F., Mondal R., Ludovico L. A., Ntalampiras S. "Optical Music Recognition in Manuscripts from the Ricordi Archive", AudioMostly 2024, Milan, Italy. DOI: https://doi.org/10.1145/3678299.3678324

Setup

  1. clone all submodules (git clone --recursive)
  2. install pyenv: curl https://pyenv.run | bash

Prepare staff-line-removal

  1. install miniconda2 PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install miniconda2-4.7.12
  2. PYENV_VERSION=miniconda2-4.7.12 conda env create -f staffline.yml
  3. PYENV_VERSION=miniconda2-4.7.12 conda init $(basename $SHELL) (you may need to fix the command substitution syntax)
  4. exec $SHELL

Prepare our project

  1. install python >= 3.9: pyenv install 3.9.16 (recommended is 3.9.16)
  2. install pdm: pipx install pdm, other info on the website
  3. install dependencies: pdm install
  4. for Jupyter, you will also need NodeJS available on your system

This will use the PyPI CUDA libraries. For using different CUDA libraries, you have to change the pyproject.toml file using the extra options provided by pytorch (see docs).

Pre-process

  1. setup the relative section in config.toml
  2. remove staff-lines: ./preprocess.sh; if it stops, you can restart it
  3. pdm preprocess

After this, you will find two files ending with _nostaff.jpg and _nostaff.json for each file in the archive, containing the image without staff and the data about that image (original file name, list of blobs, etc.). You will also find a directory ending with _nostaff for each file in the archive, containing the list of blobs, each in png format and accompanied by a .json file containing the position, the parent image path and the annotations (after the Data Entry step).

Data Entry

  • pdm data_entry

This will start the Flask app listening on all hosts requests to your machine on port 1992. You can configure the port in config.toml, as well as other options (documented in config.toml).

The server will create a 2 files:

  • __annotator.json:
    • keys are annotators (see config.toml);
    • value of each key is a list of lists where the i-th list represents the annotations given for the i-th control blob
  • __control.json:
    • keys are normal and control
    • values are lists of string with the path of the blobs in each split

Dataset analysis

  • pdm dataset_analysis

This command will run the rater agreement analysis using papermill and the notebook ./Confusion_Matrix_Annotation.ipynb

  • pdm dataset_creation (first, fix the dataset_path variable)

This command will create the dataset from the annotated files using papermill and the notebook ./Create_Dataset.ipynb. The Datasets will be in the directory ./data.

Model runs

First, check the value of the dataset_path variable.

  • pdm binary
  • pdm multiclass

These command will use papermill to run the notebooks OMR_Binary.ipynb and OMR_Multiclass.ipynb.