Abstract: Optical music recognition(OMR) is of great significance to promote the intelligence and digitization of music. The traditional music recognition process is complicated and easy to lead to the accumulation of errors, but current sequence modeling-based optical music recognition methods cannot obtain notes context information from the full scale, there is still room for improvement in the recognition effect. To this end, this paper proposes an end-to-end optical music recognition method based on residual gated recurrent convolution and attention mechanism. The method uses residual gated recurrent convolution as the backbone network to enrich the model’s ability to extract contextual information; Combined with an attention mechanism decoder, the feature information of the music score and its internal correlation can be better mined to enhance the representation ability of the model and identify the notes and notes sequences in the score image. The experimental results show that, compared with the Convolutional Recurrent Neural Network (CRNN) model, the improved network has a significant decrease in both the symbol error rate and the sequence error rate.

Key words: optical music recognition, gated recurrent convolution, attention mechanism, end-to-end