Full-Page Optical Music Recognition of Handwritten Monophonic Scores
Full-page end-to-end Optical Music Recognition seeks to transcribe entire music pages directly into symbolic notation, avoiding the limitations of traditional pipelines that rely on accurate staff segmentation. Recent Transformer-based architectures have achieved strong performance on typeset scores, relying on large-scale synthetic data for pretraining. However, their applicability to handwritten music remains largely unexplored. In this work, we study full-page transcription on handwritten monophonic collections and analyze the impact of synthetic pretraining in this setting. To investigate which factors are most relevant during pretraining, we introduce a generator capable of producing...
| Authors | Adrian Rosello, Antonio Ríos-Vila, David Rizo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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