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arxiv: 2606.09479 · v1 · pith:W57JK2JDnew · submitted 2026-06-08 · 💻 cs.CV · cs.DL

Optical Music Recognition for Real-World Manuscripts with Synthetic Data

Ji\v{r}\'i Mayer , Martina Dvo\v{r}\'akov\'a , Vojt\v{e}ch Dvo\v{r}\'ak , Mark\'eta Herz\'anov\'a Vlkov\'a , Filip B\'im , Pavel Pecina , Samuel \v{S}omorjai , Petr \v{Z}abi\v{c}ka
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Jan Haji\v{c} jr
This is my paper

Pith reviewed 2026-06-27 17:06 UTC · model grok-4.3

classification 💻 cs.CV cs.DL
keywords optical music recognitiondomain adaptationsynthetic datamusic manuscriptsMuNG annotationspiano notationheritage preservation
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The pith

Domain adaptation on synthetic manuscript images improves optical music recognition on real-world data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that existing OMR systems trained on born-digital scores fail on the diverse visual domains of real historical manuscripts. In resource-constrained settings where large in-domain labeled sets cannot be created, the authors establish a baseline for complex piano notation. They demonstrate that domain adaptation using synthetic images generated by the Smashcima tool produces significant gains, and that the symbols for synthesis can come from outside the target domain. This reduces the need for expensive fine-grained MuNG annotations while still requiring some real in-domain transcriptions. The result moves OMR toward practical application in libraries and heritage collections.

Core claim

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.

What carries the argument

Domain adaptation performed on synthetic musical manuscript images generated by the Smashcima synthesis tool together with MuNG graph annotations.

If this is right

  • Significant improvement occurs in transcription accuracy on real manuscripts.
  • Out-of-domain symbols can be used for synthesis, avoiding costly fine-grained annotation.
  • A usable baseline is now available for real-world manuscripts containing complex piano notation.
  • OMR moves closer to practical use for preserving musical cultural heritage.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same synthesis-plus-adaptation pattern may apply to other visually diverse historical document types.
  • Combining outputs from multiple synthesis pipelines could further reduce reliance on real annotations.
  • Scaling the method to larger manuscript collections would test whether the gains hold under greater domain variety.
  • The reduced annotation requirement could lower the barrier for smaller institutions to adopt OMR.

Load-bearing premise

The visual statistics of images produced by the Smashcima synthesis tool are close enough to those of real-world manuscripts that adaptation on the synthetic images transfers to the target domain.

What would settle it

Retraining the model on Smashcima synthetics and then measuring no accuracy gain over the non-adapted baseline when both are tested on the same real manuscript collection would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.09479 by Filip B\'im, Jan Haji\v{c} jr, Ji\v{r}\'i Mayer, Mark\'eta Herz\'anov\'a Vlkov\'a, Martina Dvo\v{r}\'akov\'a, Pavel Pecina, Petr \v{Z}abi\v{c}ka, Samuel \v{S}omorjai, Vojt\v{e}ch Dvo\v{r}\'ak.

Figure 1
Figure 1. Figure 1: Examples of Common Western Music Notation in a library collection. endeavour [51], it is unlikely that building sufficiently large in-domain datasets will be within the means of user institutions any time soon. An obvious next-best solution in the absence of “real” data is synthetic data. A system for OMR data synthesis needs to output two components necessary for supervised learning: (1) the musical conte… view at source ↗
Figure 2
Figure 2. Figure 2: In this paper we experiment with newly available synthetic manuscript images of sheet music to see whether they help with domain adaptation to authentic (real) manuscripts. tructure is now in place to leverage manuscript synthesis for domain adaptation to musical manuscripts, even for diverse collections of resource-constrained memory institutions. However, experimental work to show whether this pathway is… view at source ↗
Figure 3
Figure 3. Figure 3: Example annotation in the MuNG format. On the left, just the symbols are shown; note the accuracy of the symbol masks (e.g., the G-clef or the 8th flag in the first measure of the bottom staff). On the right, the annotation is shown with edges. 3.1 Perception study In a small user study, we have found that images rendered by Smashcima are barely recognisable for humans from authentic scores. We ran a surve… view at source ↗
Figure 4
Figure 4. Figure 4: Different renderings of the same MusicXML file using Smashcima [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of the variety of out-of-domain datasets [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of images from the Authentic dataset. The Authentic-MuNG dataset serves as the source of input symbols for creating synthetic data. For simplicity, we exploit the same 59 samples from the training (or fine-tuning) portion of the Authentic dataset, but annotated in the MuNG format. The total number of annotated symbols across all splits is 39,376 but only 15,011 are in the training split, which is … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript claims to provide the first baseline for OMR on real-world manuscripts with complex piano notation in a resource-constrained setting. It uses MuNG annotations and the Smashcima synthesis tool to show that domain adaptation with synthetic musical manuscript images yields significant improvement, and that the symbols in the synthetic data do not need to be in-domain, thereby avoiding expensive fine-grained annotation.

Significance. If the central claims hold, this work is significant because it addresses the gap between born-digital training data and diverse real manuscript domains in OMR, which is critical for applying the technology to cultural heritage preservation. The approach of using synthetic data for adaptation without requiring in-domain symbols is a practical strength that could reduce annotation costs. The provision of a baseline in the resource-constrained scenario is valuable for the field.

major comments (1)
  1. [Results section] Results section: No distribution-distance metric (e.g., FID, MMD, or feature-space divergence) is reported between the Smashcima synthetic training images and the real-world manuscript test set. Without this, it is unclear whether the observed performance gains are due to successful domain adaptation or confounds such as differences in data volume or annotation quality, undermining the attribution of improvement to the synthesis-based adaptation.
minor comments (1)
  1. [Abstract] The abstract states that domain adaptation 'brings significant improvement' but does not include any quantitative results, baselines, or error bars, making it difficult to assess the magnitude of the claimed gains from the summary alone.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on the Results section. We address it point by point below.

read point-by-point responses

  1. Referee: [Results section] Results section: No distribution-distance metric (e.g., FID, MMD, or feature-space divergence) is reported between the Smashcima synthetic training images and the real-world manuscript test set. Without this, it is unclear whether the observed performance gains are due to successful domain adaptation or confounds such as differences in data volume or annotation quality, undermining the attribution of improvement to the synthesis-based adaptation.

    Authors: We acknowledge that no distribution-distance metric is reported. Our experimental design controls for data volume by using identical numbers of training images in each compared condition (born-digital only, synthetic manuscript images only, and mixtures). All conditions are evaluated on the identical fixed real-world test set, so the sole systematic difference is the visual domain of the training data. Annotation quality is likewise controlled in the sense that synthetic data supplies exact ground-truth labels generated by the Smashcima pipeline while real data uses the same MuNG annotation protocol; any performance lift when synthetic manuscript images are added therefore cannot be explained by annotation differences alone. We will revise the manuscript to make these controls explicit and, where feasible, add a feature-space divergence analysis between the synthetic and real image sets. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical gains from external synthesis tool and standard adaptation

full rationale

The paper reports empirical improvements from training OMR models on synthetic manuscript images produced by the Smashcima tool and applying domain adaptation to real-world targets. No equations, fitted parameters, or predictions are described that reduce to the inputs by construction. Claims rest on external synthesis software and conventional adaptation pipelines rather than self-definitional steps or load-bearing self-citations that would force the reported outcome. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain-assumption that Smashcima-generated images capture sufficient visual variation to enable transfer; no free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Synthetic images from Smashcima are distributionally close enough to real manuscripts for domain adaptation to succeed
    Invoked when claiming that adaptation on synthetic data produces significant improvement on real data.

pith-pipeline@v0.9.1-grok · 5793 in / 1224 out tokens · 29762 ms · 2026-06-27T17:06:41.266399+00:00 · methodology

discussion (0)

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Reference graph

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