Optical Music Recognition for Real-World Manuscripts with Synthetic Data

• URL: http://arxiv.org/abs/2606.09479v1
• Date: Mon, 08 Jun 2026 13:38:48 GMT
• Title: Optical Music Recognition for Real-World Manuscripts with Synthetic Data
• Abstract summary: We provide a baseline on real-world manuscripts with complex piano notation.<n>We then show that while some direct transcriptions of in-domain data remain essential, domain adaptation using synthetic musical manuscript images brings significant improvement.<n>We thus bring Optical Music Recognition closer to one of its stated goals: preserving and promoting musical cultural heritage.
• Score: 1.3125176461810544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical Music Recognition (OMR) has seen major progress in model design, with end-to-end methods now capable of recognising notation at all levels of complexity. However, the impact of this progress has been limited by the visual domains of available training datasets, which are largely born-digital. Existing large collections of sheet music in libraries and other heritage institutions contain predominantly manuscripts, whose visual domains are highly diverse and different, so existing OMR systems fail when applied in the real world. These institutions are often resource-constrained, so large in-domain datasets cannot be expected. We provide a first baseline on real-world manuscripts with complex piano notation in the resource-constrained scenario. Using fine-grained music notation graph (MuNG) annotations and the Smashcima synthesis tool, we then show that while some direct transcriptions of in-domain data remain essential, domain adaptation using synthetic musical manuscript images brings significant improvement. Furthermore, the symbols used do not need to be in-domain, so the expensive fine-grained annotation can be avoided. We thus bring OMR closer to one of its stated goals: preserving and promoting musical cultural heritage.

Related papers

• FIGMA: Towards FIne-Grained Music retrievAl [65.98380295254817]
  Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries.<n>We show that this limitation stems from the contrastive learning objective itself: despite being trained on long captions, CLAP-based models effectively utilize only the first few tokens.<n>We propose FIGMA, a multi-view contrastive architecture that addresses this limitation by jointly optimizing global audio-text alignment and frame-level, token-wise alignment.
  arXiv  Detail & Related papers  (2026-06-04T18:05:39Z)
• A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation [2.3800349692393814]
  MusiCorpus is the largest dataset of handwritten music to date.<n>It is suitable for training and evaluating both end-to-end and object detection-based Optical Music Recognition systems.
  arXiv  Detail & Related papers  (2026-05-18T14:07:44Z)
• GAN-based Content-Conditioned Generation of Handwritten Musical Symbols [5.69735546372407]
  This study explores the generation of realistic, handwritten-looking scores by implementing a music symbol-level Generative Adversarial Network (GAN)<n>We have evaluated the visual fidelity of these generated samples, concluding that the generated symbols exhibit a high degree of realism.
  arXiv  Detail & Related papers  (2025-10-16T11:21:53Z)
• Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning [69.78158549955384]
  We introduce a novel approach that treats core music theory rules, such as those governing beats and intervals, as programmatic functions.<n>This approach generates verifiable sheet music questions in both textual and visual modalities.<n> Evaluation results on SSMR-Bench highlight the key role reasoning plays in interpreting sheet music.
  arXiv  Detail & Related papers  (2025-09-04T09:42:17Z)
• MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
  We are inspired by how musicians compose music not just from a movie script, but also through visualizations. We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
  arXiv  Detail & Related papers  (2024-06-07T06:38:59Z)
• End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music [12.779526750915707]
  We present the first truly end-to-end approach for page-level Optical Music Recognition in complex layouts.<n>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.<n>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.
  arXiv  Detail & Related papers  (2024-05-20T15:21:48Z)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• TrOMR:Transformer-Based Polyphonic Optical Music Recognition [26.14383240933706]
  We propose a transformer-based approach with excellent global perceptual capability for end-to-end polyphonic OMR, called TrOMR. We also introduce a novel consistency loss function and a reasonable approach for data annotation to improve recognition accuracy for complex music scores.
  arXiv  Detail & Related papers  (2023-08-18T08:06:27Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• RMSSinger: Realistic-Music-Score based Singing Voice Synthesis [56.51475521778443]
  RMS-SVS aims to generate high-quality singing voices given realistic music scores with different note types. We propose RMSSinger, the first RMS-SVS method, which takes realistic music scores as input. In RMSSinger, we introduce word-level modeling to avoid the time-consuming phoneme duration annotation and the complicated phoneme-level mel-note alignment.
  arXiv  Detail & Related papers  (2023-05-18T03:57:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.