# Practical End-to-End Optical Music Recognition for Pianoform Music

arXiv: https://arxiv.org/abs/2403.13763 · HTML: https://mlsanity.com/paper/2403.13763/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [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)
- [Understanding Optical Music Recognition](https://mlsanity.com/paper/1908.03608/) (2019)

## Built on by (most cited first, 9 of 9)

- [End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music](https://mlsanity.com/paper/2405.12105/) (2024)
- [Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio](https://mlsanity.com/paper/2505.12863/) (2025)
- [MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2506.23009/) (2025)
- [LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR](https://mlsanity.com/paper/2506.19065/) (2025)
- [Optical Music Recognition for Real-World Manuscripts with Synthetic Data](https://mlsanity.com/paper/2606.09479/) (2026)
- [A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation](https://mlsanity.com/paper/2605.18436/) (2026)
- [Towards a foundational model for recognising diastematic Gregorian notation](https://mlsanity.com/paper/2606.31454/) (2026)
- [LEGATO 2: Toward Multimodal Sheet Music Recognition and Understanding](https://mlsanity.com/paper/2607.05769/) (2026)
- [A Dataset and Benchmark for Optical Music Recognition of String Quartet Scores](https://mlsanity.com/paper/2608.10978/) (2026)

## Abstract (arXiv)

The majority of recent progress in Optical Music Recognition (OMR) has been achieved with Deep Learning methods, especially models following the end-to-end paradigm, reading input images and producing a linear sequence of tokens. Unfortunately, many music scores, especially piano music, cannot be easily converted to a linear sequence. This has led OMR researchers to use custom linearized encodings, instead of broadly accepted structured formats for music notation. Their diversity makes it difficult to compare the performance of OMR systems directly. To bring recent OMR model progress closer to useful results: (a) We define a sequential format called Linearized MusicXML, allowing to train an end-to-end model directly and maintaining close cohesion and compatibility with the industry-standard MusicXML format. (b) We create a dev and test set for benchmarking typeset OMR with MusicXML ground truth based on the OpenScore Lieder corpus. They contain 1,438 and 1,493 pianoform systems, each with an image from IMSLP. (c) We train and fine-tune an end-to-end model to serve as a baseline on the dataset and employ the TEDn metric to evaluate the model. We also test our model against the recently published synthetic pianoform dataset GrandStaff and surpass the state-of-the-art results.