From Optical Music Recognition to Handwritten Music Recognition: A baseline

From Optical Music Recognition to Handwritten Music Recognition: A baseline. Baró, A., Riba, P., Calvo-Zaragoza, J., & Fornés, A. Pattern Recognit. Lett., 123:1 – 8, 2019.

Paper doi abstract bibtex

Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.

@article{baro_optical_2019,
	title = {From {Optical} {Music} {Recognition} to {Handwritten} {Music} {Recognition}: {A} baseline},
	volume = {123},
	issn = {0167-8655},
	url = {http://www.sciencedirect.com/science/article/pii/S0167865518303386},
	doi = {10.1016/j.patrec.2019.02.029},
	abstract = {Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.},
	journal = {Pattern Recognit. Lett.},
	author = {Baró, Arnau and Riba, Pau and Calvo-Zaragoza, Jorge and Fornés, Alicia},
	year = {2019},
	keywords = {\#nosource, Deep neural networks, Document image analysis and recognition, Handwritten music recognition, LSTM, Optical music recognition},
	pages = {1 -- 8},
}

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