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 that was used for the experiments reported in the paper End-to-End Neural Optical Music Recognition of Monophonic Scores. 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.
The two parts of the projects can be tested using the corresponding notebooks AE2E.ipynb and Staff_Removal.ipynb
@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}
}