A. Fornés and G. Sánchez, Analysis and Recognition of Music Scores, Handbook of Document Image Processing, pp.749-774, 2014.

A. Rebelo, I. Fujinaga, F. Paszkiewicz, A. R. Marcal, C. Guedes et al., Optical music recognition: State-of-the-art and open issues, International Journal of Multimedia Information Retrieval, vol.1, issue.3, pp.173-190, 2012.

J. Calvo-zaragoza, G. Vigliensoni, and I. Fujinaga, Staff-Line Detection on Grayscale Images with Pixel Classification, SpringerLink, pp.279-286, 2017.

V. P. Andecy, J. Camillerapp, and I. Leplumey, Kalman filtering for segment detection: Application to music scores analysis, Proceedings of 12th International Conference on Pattern Recognition, vol.1, pp.301-305, 1994.

A. Rebelo, G. Capela, and J. S. Cardoso, Optical recognition of music symbols, International Journal on Document Analysis and Recognition (IJDAR), vol.13, issue.1, pp.19-31, 2009.

A. Pacha, K. Choi, B. Coüasnon, Y. Ricquebourg, R. Zanibbi et al., Handwritten Music Object Detection: Open Issues and Baseline Results, 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp.163-168, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01972424

J. Haji? and P. Pecina, The MUSCIMA++ Dataset for Handwritten Optical Music Recognition, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol.01, pp.39-46, 2017.

A. Pacha and J. Calvo-zaragoza, Optical Music Recognition in Mensural Notation with Region-Based Convolutional Neural Networks, in ISMIR, 2018.

J. Hajic, M. Dorfer, G. Widmer, and P. Pecina, Towards Full-Pipeline Handwritten OMR with Musical Symbol Detection by U-Nets, in ISMIR, 2018.

L. Tuggener, I. Elezi, J. Schmidhuber, and T. Stadelmann, Deep Watershed Detector for Music Object Recognition, 19th International Society for Music Information Retrieval Conference, pp.23-27, 2018.

B. Coüasnon, DMOS : A generic document recognition method, application to an automatic generator of musical scores, mathematical formulae and table structures recognition systems, Sixth International Conference on Document Analysis and Recognition, pp.215-220, 2001.

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, Spatial Transformer Networks, Advances in Neural Information Processing Systems, vol.28, pp.2017-2025, 2015.

S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems, vol.28, pp.91-99, 2015.

Y. Dai, K. Li, J. He, and . Sun, R-FCN: Object Detection via Region-based Fully Convolutional Networks, Advances in Neural Information Processing Systems, vol.29, pp.379-387, 2016.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed et al., SSD: Single Shot MultiBox Detector, 14th European Conference on Computer Vision, vol.9905, pp.21-37, 2016.

J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara et al., Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7310-7311, 2017.

M. Everingham, L. V. Gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.88, issue.2, pp.303-338, 2010.