# Understanding Optical Music Recognition

arXiv: https://arxiv.org/abs/1908.03608 · HTML: https://mlsanity.com/paper/1908.03608/

## Key facts

- Cited by 37 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 30 of 37)

- [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://mlsanity.com/paper/2409.01704/) (2024)
- [Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription](https://mlsanity.com/paper/2402.07596/) (2024)
- [End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music](https://mlsanity.com/paper/2405.12105/) (2024)
- [DoReMi: First glance at a universal OMR dataset](https://mlsanity.com/paper/2107.07786/) (2021)
- [Practical End-to-End Optical Music Recognition for Pianoform Music](https://mlsanity.com/paper/2403.13763/) (2024)
- [An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition](https://mlsanity.com/paper/2108.01769/) (2021)
- [Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation](https://mlsanity.com/paper/2506.10488/) (2025)
- [A Unified Representation Framework for the Evaluation of Optical Music Recognition Systems](https://mlsanity.com/paper/2312.12908/) (2023)
- [Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio](https://mlsanity.com/paper/2505.12863/) (2025)
- [Region-based Layout Analysis of Music Score Images](https://mlsanity.com/paper/2201.04214/) (2022)
- [MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2506.23009/) (2025)
- [Late multimodal fusion for image and audio music transcription](https://mlsanity.com/paper/2204.03063/) (2022)
- [Toward a More Complete OMR Solution](https://mlsanity.com/paper/2409.00316/) (2024)
- [Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN](https://mlsanity.com/paper/2411.16405/) (2024)
- [Optical Music Recognition in Manuscripts from the Ricordi Archive](https://mlsanity.com/paper/2408.10260/) (2024)
- [Optical Music Recognition of Jazz Lead Sheets](https://mlsanity.com/paper/2509.05329/) (2025)
- [Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems](https://mlsanity.com/paper/2309.12134/) (2023)
- [Towards Robust and Truly Large-Scale Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12158/) (2023)
- [Direct content-based retrieval from music scores images](https://mlsanity.com/paper/2605.22255/) (2026)
- [Optical Music Recognition for Real-World Manuscripts with Synthetic Data](https://mlsanity.com/paper/2606.09479/) (2026)
- [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)
- [Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12111/) (2023)
- [Proceedings of the 5th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2311.04091/) (2023)
- [KuiSCIMA v2.0: Improved Baselines, Calibration, and Cross-Notation Generalization for Historical Chinese Music Notations in Jiang Kui's Baishidaoren Gequ](https://mlsanity.com/paper/2507.18741/) (2025)
- [GAN-based Content-Conditioned Generation of Handwritten Musical Symbols](https://mlsanity.com/paper/2510.17869/) (2025)
- [A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions](https://mlsanity.com/paper/2604.16446/) (2026)
- [From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR](https://mlsanity.com/paper/2604.20522/) (2026)
- [MusicSynth: An Automated Pipeline for Generating Violin Fingerboard Animations from Sheet Music Using Optical Music Recognition](https://mlsanity.com/paper/2605.17181/) (2026)

## Abstract (arXiv)

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: few introductory materials are available, and furthermore the field has struggled with defining itself and building a shared terminology. In this tutorial, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.