https://github.com/freshmag/omr-img2midi

Optical Music Recognition project to perform translations from music sheets images to music encoding formats (MIDI, MEI ecc.)
https://github.com/freshmag/omr-img2midi

ai computer-vision machine-learning omr opencv

Last synced: 2 months ago
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Optical Music Recognition project to perform translations from music sheets images to music encoding formats (MIDI, MEI ecc.)

• Host: GitHub
• URL: https://github.com/freshmag/omr-img2midi
• Owner: FreshMag
• License: mit
• Created: 2024-03-07T13:55:37.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-05-15T13:14:18.000Z (about 2 years ago)
• Last Synced: 2025-04-14T17:14:37.746Z (about 1 year ago)
• Topics: ai, computer-vision, machine-learning, omr, opencv
• Language: Python
• Homepage:
• Size: 5.89 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# Optical Music Recognition (img2midi)

Staff Removal: 

  



End-To-End: 

  



Computer vision project where different algorithms and methodologies of Optical Music Recognition are studied and implemented.

This repository is a fork of the [original project by Calvo-Zaragoza](https://github.com/OMR-Research/tf-end-to-end) that

was used for the experiments reported in the paper [End-to-End Neural Optical Music Recognition of Monophonic Scores](http://www.mdpi.com/2076-3417/8/4/606).

More information can be found below. Please consider taking a look at the original repository if you want to know all

the details about how the model was originally trained and created.

The original code has been deeply extended and modified, adding much more functionalities described in the report.

## Usage

The two parts of the projects can be tested using the corresponding notebooks `AE2E.ipynb` and `Staff_Removal.ipynb`

## Citations

```

@Article{Calvo-Zaragoza2018,

  AUTHOR = {Calvo-Zaragoza, Jorge and Rizo, David},

  TITLE = {End-to-End Neural Optical Music Recognition of Monophonic Scores},

  JOURNAL = {Applied Sciences},

  VOLUME = {8},

  YEAR = {2018},

  NUMBER = {4},

  ARTICLE NUMBER = {606},

  URL = {http://www.mdpi.com/2076-3417/8/4/606},

  ISSN = {2076-3417},

  DOI = {10.3390/app8040606}

}

```