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

arXiv: https://arxiv.org/abs/2604.16446 · HTML: https://mlsanity.com/paper/2604.16446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Understanding Optical Music Recognition](https://mlsanity.com/paper/1908.03608/) (2019)
- [Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription](https://mlsanity.com/paper/2402.07596/) (2024)
- [Optical Music Recognition with Convolutional Sequence-to-Sequence Models](https://mlsanity.com/paper/1707.04877/) (2017)
- [An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition](https://mlsanity.com/paper/2108.01769/) (2021)
- [Sheet Music Benchmark: Standardized Optical Music Recognition Evaluation](https://mlsanity.com/paper/2506.10488/) (2025)
- [Residual Recurrent CRNN for End-to-End Optical Music Recognition on Monophonic Scores](https://mlsanity.com/paper/2010.13418/) (2020)
- [Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach](https://mlsanity.com/paper/2411.16408/) (2024)

## Abstract (arXiv)

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.