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MasonDill/Monophonic_OMR
Monophonic_OMR is a machine learning model from MasonDill. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
TensorFlow model to perform end-to-end Optical Music Recognition on monophonic scores through Convolutional Recurrent Neural Networks and CTC-based training.
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Updated Nov 7, 2023
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From the Hugging Face model README
TensorFlow model to perform end-to-end Optical Music Recognition on monophonic scores through Convolutional Recurrent Neural Networks and CTC-based training.
End-to-End Neural Optical Music Recognition of Monophonic Scores
@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}
}
This repository is intended for the Printed Images of Music Staves (PrIMuS) dataset.
PrIMuS can be donwloaded from https://grfia.dlsi.ua.es/primus/
The source code used to generate the models, and examples on how to use it, can be found at https://github.com/MasonDill/tf-end-to-end
This code was altered from https://github.com/OMR-Research/tf-end-to-end
Models from the origonal author can be found here:
These models were the result of the traning process for one of the folds of the 10-fold cross-validation considered in the paper.
Let's see an example for the sample from PrIMuS provided in Data/Example:

This sample belongs to the test set of the aforementioned fold, so it was not seen by the networks during their training stage.