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A. Pacha, K. Choi, B. Coüasnon, Y. Ricquebourg, H. Eidenberger:
"Handwritten Music Object Detection: Open Issues and Baseline Results";
Vortrag: 2018 13th IAPR Workshop on Document Analysis Systems (DAS), Wien; 04/2018; in: "2018 13th IAPR Workshop on Document Analysis Systems (DAS)", (2018), 6 S.

Optical Music Recognition (OMR) is the challenge of understanding the content of musical scores. Accurate detection of individual music objects is a critical step in
processing musical documents because a failure at this stage
corrupts any further processing. So far, all proposed methods
were either limited to typeset music scores or were built to
detect only a subset of the available classes of music symbols.
In this work, we propose an end-to-end trainable object detector for music symbols that is capable of detecting almost
the full vocabulary of modern music notation in handwritten
music scores. By training deep convolutional neural networks
on the recently released MUSCIMA++ dataset which has
symbol-level annotations, we show that a machine learning approach can be used to accurately detect music objects with
a mean average precision of over 80%.

Optical Music Recognition (OMR) is the challenge of understanding the content of musical scores. Accurate detection of individual music objects is a critical step in
processing musical documents because a failure at this stage
corrupts any further processing. So far, all proposed methods
were either limited to typeset music scores or were built to
detect only a subset of the available classes of music symbols.
In this work, we propose an end-to-end trainable object detector for music symbols that is capable of detecting almost
the full vocabulary of modern music notation in handwritten
music scores. By training deep convolutional neural networks
on the recently released MUSCIMA++ dataset which has
symbol-level annotations, we show that a machine learning approach can be used to accurately detect music objects with
a mean average precision of over 80%.

Optical Music Recognition; Object Detection; Handwritten Scores; Deep Learning

http://dx.doi.org/10.1109/DAS.2018.51

https://publik.tuwien.ac.at/files/publik_269897.pdf