<a href="" rel="noopener"> <img width=400px height=210px src="https://github.com/aashrafh/mozart/blob/main/logo.svg" alt="Mozart logo"></a> :notes: Convert sheet music to a machine-readable version. <a href="https://github.com/aashrafh/mozart/graphs/contributors" alt="Contributors"> <img src="https://img.shields.io/github/contributors/aashrafh/mozart" /></a> <a href="https://github.com/aashrafh/mozart/issues" alt="Issues"> <img src="https://img.shields.io/github/issues/aashrafh/mozart" /></a> <a href="https://github.com/aashrafh/mozart/network" alt="Forks"> <img src="https://img.shields.io/github/forks/aashrafh/mozart" /></a> <a href="https://github.com/aashrafh/mozart/stargazers" alt="Stars"> <img src="https://img.shields.io/github/stars/aashrafh/mozart" /></a> <a href="https://github.com/aashrafh/mozart/blob/master/LICENSE" alt="License"> <img src="https://img.shields.io/github/license/aashrafh/mozart" /></a>

🧐 About <a name = "about"></a>

The aim of this project is to develop a sheet music reader. This is called Optical Music Recognition (OMR). Its objective is to convert sheet music to a machine-readable version. We take a simplified version where we convert an image of sheet music to a textual representation that can be further processed to produce midi files or audio files like wav or mp3.

:computer: Methodology <a name = "methodology"></a>

1. Noise Filtering and Binarization

2. Segmentation

3. Staff Line Detection and Removal

4. Construct The New Staff Lines

5. Symbol Detection and Recognition

🏁 Install <a name = "Install"></a>

You can use the attached notebook for quick testing and visualization.
You can setup an environment on your local machine to run the project:
1. Install Conda
2. conda env create -f requirements.yml
3. conda activate mozart
4. python3 main.py <input directory path> <output directory path>

You can find the dataset on Google Drive.

Please check the following issue for another requirements.yml file.

Mozart

Install / Use

README