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End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music
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End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music
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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.
Forward citations
Cited by 6 Pith papers
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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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Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio
A unified Transformer model with modality-specific tokenization, trained on a new 1300-hour multimodal music dataset, outperforms single-task baselines on optical music recognition and other translations while achievi...
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Direct content-based retrieval from music scores images
Compares OMR-based, direct transformer, and LLM approaches for content-based retrieval in music score images across four corpora, finding OMR stronger in-domain and transcription-free models better for variability.
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Direct content-based retrieval from music scores images
Evaluates OMR-based, transcription-free Transformer, and LLM approaches for content-based retrieval in music score images on four diverse corpora, concluding OMR excels in-domain while transcription-free models handle...
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A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions
A CNN using ResNet-v2-style residual bottleneck blocks and multi-scale dilated convolutions followed by BiGRU and CTC loss achieves SeER of 7.52% and SyER of 0.45% on the Camera-PrIMuS dataset for optical music recognition.
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