# Optical Music Recognition: State of the Art and Major Challenges

arXiv: https://arxiv.org/abs/2006.07885 · HTML: https://mlsanity.com/paper/2006.07885/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

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

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [DoReMi: First glance at a universal OMR dataset](https://mlsanity.com/paper/2107.07786/) (2021)
- [Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation](https://mlsanity.com/paper/2506.10488/) (2025)
- [TrOMR:Transformer-Based Polyphonic Optical Music Recognition](https://mlsanity.com/paper/2308.09370/) (2023)
- [Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation](https://mlsanity.com/paper/2408.15002/) (2024)
- [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)
- [Listen to Your Favorite Melodies with img2Mxml, Producing MusicXML from Sheet Music Image by Measure-based Multimodal Deep Learning-driven Assembly](https://mlsanity.com/paper/2106.12037/) (2021)
- [Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach](https://mlsanity.com/paper/2411.16408/) (2024)
- [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 5th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2311.04091/) (2023)

## Abstract (arXiv)

Optical Music Recognition (OMR) is concerned with transcribing sheet music into a machine-readable format. The transcribed copy should allow musicians to compose, play and edit music by taking a picture of a music sheet. Complete transcription of sheet music would also enable more efficient archival. OMR facilitates examining sheet music statistically or searching for patterns of notations, thus helping use cases in digital musicology too. Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach. In this paper, we review relevant works in OMR, including fundamental methods and significant outcomes, and highlight different stages of the OMR pipeline. These stages often lack standard input and output representation and standardised evaluation. Therefore, comparing different approaches and evaluating the impact of different processing methods can become rather complex. This paper provides recommendations for future work, addressing some of the highlighted issues and represents a position in furthering this important field of research.