Optical music recognition: state-of-the-art and open issues

By Ana Rebelo, Ichiro Fujinaga, Filipe Paszkiewicz, André R. S. Marçal, Carlos Guedes, Jaime S. Cardoso. International Journal of Multimedia Information Retrieval, 2012

308 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/88b6c06624090aab784ce096e58d45b7c1eea918

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Cong Yao
2. Xiang Bai
3. Baoguang Shi
4. Minghui Liao
5. Xin He
6. Chunhua Shen
7. Pengyuan Lyu
8. Shangbang Long

In Signal Processing

1. Cong Yao
2. Xiang Bai
3. Baoguang Shi
4. Thilo Stadelmann
5. Minghui Liao

Citations in the corpus

• Deep Learning in the Wild (2018, background): “Optical music recognition (OMR) [46] is the process of translating an image of a page of sheet music into a machine-readable structured format like MusicXML.”
• A comprehensive survey of mostly textual document segmentation algorithms since 2008 (2017, background): “staff removal and direct recognition of music scores without any layout analysis [23].”
• An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition (2015, methodology): “Previous methods often requires image preprocessing (mostly binirization), staff lines detection and individual notes recognition [39].”