A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions

arXiv:2604.16446v1 Announce Type: new Abstract: Optical Music Recognition (OMR) aims to convert printed or handwritten music score images into editable symbolic representations. This paper presents an end-to-end OMR framework that combines residual bottleneck convolutions with bidirectional gated recurrent unit (BiGRU)-based sequence modeling. A convolutional neural network with ResNet-v2-style residual bottleneck blocks and multi-scale dilated convolutions is used to extract features that encode both fine-grained symbol details and global staff-line structures. The extracted feature sequences are then fed into a BiGRU network to model temporal dependencies among musical symbols. The model is trained using the Connectionist Temporal Classification loss, enabling end-to-end prediction without explicit alignment annotations. Experimental results on the Camera-PrIMuS and PrIMuS datasets demonstrate the effectiveness of the proposed framework. On Camera-PrIMuS, the proposed method achieves a sequence error rate (SeER) of 7.52% and a symbol error rate (SyER) of 0.45%, with pitch, type, and note accuracies of 99.33%, 99.60%, and 99.28%, respectively. The average training time is 1.74~s per epoch, demonstrating high computational efficiency while maintaining strong recognition performance. On PrIMuS, the method achieves a SeER of 8.11% and a SyER of 0.49%, with pitch, type, and note accuracies of 99.27%, 99.58%, and 99.21%, respectively. A fine-grained error analysis further confirms the effectiveness of the proposed model.

Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images

Source: arXiv cs.CV | 2026-04-21

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