# Optical Music Recognition with Convolutional Sequence-to-Sequence Models

arXiv: https://arxiv.org/abs/1707.04877 · HTML: https://mlsanity.com/paper/1707.04877/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2017.

## Built on by (most cited first, 12 of 12)

- [An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition](https://mlsanity.com/paper/2108.01769/) (2021)
- [TrOMR:Transformer-Based Polyphonic Optical Music Recognition](https://mlsanity.com/paper/2308.09370/) (2023)
- [MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2506.23009/) (2025)
- [Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN](https://mlsanity.com/paper/2411.16405/) (2024)
- [A framework to compare music generative models using automatic evaluation metrics extended to rhythm](https://mlsanity.com/paper/2101.07669/) (2021)
- [Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems](https://mlsanity.com/paper/2309.12134/) (2023)
- [Proceedings of the 4th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2211.13285/) (2022)
- [Proceedings of the 3rd International Workshop on Reading Music Systems](https://mlsanity.com/paper/2212.00378/) (2022)
- [Proceedings of the 2nd International Workshop on Reading Music Systems](https://mlsanity.com/paper/2212.00380/) (2022)
- [Proceedings of the 1st International Workshop on Reading Music Systems](https://mlsanity.com/paper/2301.10062/) (2022)
- [Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12111/) (2023)
- [A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions](https://mlsanity.com/paper/2604.16446/) (2026)

## Abstract (arXiv)

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.