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.
Calvo-Zaragoza, J
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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.