# End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music

arXiv: https://arxiv.org/abs/2405.12105 · HTML: https://mlsanity.com/paper/2405.12105/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [Understanding Optical Music Recognition](https://mlsanity.com/paper/1908.03608/) (2019)
- [Full Page Handwriting Recognition via Image to Sequence Extraction](https://mlsanity.com/paper/2103.06450/) (2021)
- [Practical End-to-End Optical Music Recognition for Pianoform Music](https://mlsanity.com/paper/2403.13763/) (2024)
- [MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition](https://mlsanity.com/paper/2303.13931/) (2023)
- [TrOMR:Transformer-Based Polyphonic Optical Music Recognition](https://mlsanity.com/paper/2308.09370/) (2023)
- [Region-based Layout Analysis of Music Score Images](https://mlsanity.com/paper/2201.04214/) (2022)
- [musicaiz: A Python Library for Symbolic Music Generation, Analysis and Visualization](https://mlsanity.com/paper/2209.07974/) (2022)

## Built on by (most cited first, 13 of 13)

- [Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation](https://mlsanity.com/paper/2506.10488/) (2025)
- [Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio](https://mlsanity.com/paper/2505.12863/) (2025)
- [Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning](https://mlsanity.com/paper/2509.04059/) (2025)
- [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)
- [Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training](https://mlsanity.com/paper/2605.10835/) (2026)
- [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)
- [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)
- [MuSViT: A Foundation Vision Model for Sheet Music Representation](https://mlsanity.com/paper/2606.31811/) (2026)
- [A Dataset and Benchmark for Optical Music Recognition of String Quartet Scores](https://mlsanity.com/paper/2608.10978/) (2026)
- [Full-Page Optical Music Recognition of Handwritten Monophonic Scores](https://mlsanity.com/paper/2609.05662/) (2026)

## Abstract (arXiv)

Optical Music Recognition (OMR) has made significant progress since its inception, with various approaches now capable of accurately transcribing music scores into digital formats. Despite these advancements, most so-called end-to-end OMR approaches still rely on multi-stage processing pipelines for transcribing full-page score images, which entails challenges such as the need for dedicated layout analysis and specific annotated data, thereby limiting the general applicability of such methods. In this paper, we present the first truly end-to-end approach for page-level OMR in complex layouts. Our system, which combines convolutional layers with autoregressive Transformers, processes an entire music score page and outputs a complete transcription in a music encoding format. This is made possible by both the architecture and the training procedure, which utilizes curriculum learning through incremental synthetic data generation. We evaluate the proposed system using pianoform corpora, which is one of the most complex sources in the OMR literature. This evaluation is conducted first in a controlled scenario with synthetic data, and subsequently against two real-world corpora of varying conditions. Our approach is compared with leading commercial OMR software. The results demonstrate that our system not only successfully transcribes full-page music scores but also outperforms the commercial tool in both zero-shot settings and after fine-tuning with the target domain, representing a significant contribution to the field of OMR.