The MUSCIMA++ Dataset for Handwritten Optical Music Recognition

@article{Hajic2017TheMD,
  title={The MUSCIMA++ Dataset for Handwritten Optical Music Recognition},
  author={Jan Hajic and Pavel Pecina},
  journal={2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
  year={2017},
  volume={01},
  pages={39-46}
}
Optical Music Recognition (OMR) promises to make accessible the content of large amounts of musical documents, an important component of cultural heritage. However, the field does not have an adequate dataset and ground truth for benchmarking OMR systems, which has been a major obstacle to measurable progress. Furthermore, machine learning methods for OMR require training data. We design and collect MUSCIMA++, a new dataset for OMR. Ground truth in MUSCIMA++ is a notation graph, which our… CONTINUE READING

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