Domain adaptation via synthetic manuscript images improves OMR performance on real-world piano manuscripts without requiring in-domain symbols.
Calvo-Zaragoza, J
3 Pith papers cite this work, alongside 100 external citations. Polarity classification is still indexing.
3
Pith papers citing it
100
external citations · OpenAlex
years
2026 3representative citing papers
System-by-system autoregressive OMR with text-aware ABC transcription outperforms prior neural and rule-based systems and boosts VLM sheet-music QA.
A two-stage OMR pipeline decodes symbol candidates into polyphonic score structures via topology recognition with probability-guided search.
citing papers explorer
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Optical Music Recognition for Real-World Manuscripts with Synthetic Data
Domain adaptation via synthetic manuscript images improves OMR performance on real-world piano manuscripts without requiring in-domain symbols.
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LEGATO 2: Toward Multimodal Sheet Music Recognition and Understanding
System-by-system autoregressive OMR with text-aware ABC transcription outperforms prior neural and rule-based systems and boosts VLM sheet-music QA.
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From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR
A two-stage OMR pipeline decodes symbol candidates into polyphonic score structures via topology recognition with probability-guided search.