Handwritten Music Object Detection: Open Issues and Baseline Results

@article{Pacha2018HandwrittenMO,
  title={Handwritten Music Object Detection: Open Issues and Baseline Results},
  author={Alexander Pacha and Kwon-Young Choi and Bertrand Co{\"u}asnon and Yann Ricquebourg and Richard Zanibbi and Horst M. Eidenberger},
  journal={2018 13th IAPR International Workshop on Document Analysis Systems (DAS)},
  year={2018},
  pages={163-168}
}
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… CONTINUE READING

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Key Quantitative Results

  • 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%.

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