AI RESEARCH
A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions
arXiv CS.LG
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ArXi:2604.16446v1 Announce Type: cross 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.