# Proceedings of the 3rd International Workshop on Reading Music Systems

arXiv: https://arxiv.org/abs/2212.00378 · HTML: https://mlsanity.com/paper/2212.00378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 128 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Understanding Optical Music Recognition](https://mlsanity.com/paper/1908.03608/) (2019)
- [A selectional auto-encoder approach for document image binarization](https://mlsanity.com/paper/1706.10241/) (2017)
- [DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects](https://mlsanity.com/paper/1804.00525/) (2018)
- [Optical Music Recognition with Convolutional Sequence-to-Sequence Models](https://mlsanity.com/paper/1707.04877/) (2017)
- [Optical Music Recognition: State of the Art and Major Challenges](https://mlsanity.com/paper/2006.07885/) (2020)
- [Deep Watershed Detector for Music Object Recognition](https://mlsanity.com/paper/1805.10548/) (2018)
- [Learning to Read and Follow Music in Complete Score Sheet Images](https://mlsanity.com/paper/2007.10736/) (2020)
- [Unsupervised Neural Domain Adaptation for Document Image Binarization](https://mlsanity.com/paper/2012.01204/) (2020)

## Abstract (arXiv)

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.