Domain adaptation via synthetic manuscript images improves OMR performance on real-world piano manuscripts without requiring in-domain symbols.
arXiv preprint arXiv:2402.07596 (2024) 7
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GOT is a unified end-to-end model that treats all man-made optical signals as characters and handles multiple OCR tasks including formatted output and interactive region recognition via prompts.
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.
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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General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
GOT is a unified end-to-end model that treats all man-made optical signals as characters and handles multiple OCR tasks including formatted output and interactive region recognition via prompts.
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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.