  One of the challenges of the Optical Music Recognition task is to transcript
the symbols of the camera-captured images into digital music notations.
Previous end-to-end model which was developed as a Convolutional Recurrent
Neural Network does not explore sufficient contextual information from full
scales and there is still a large room for improvement. We propose an
innovative framework that combines a block of Residual Recurrent Convolutional
Neural Network with a recurrent Encoder-Decoder network to map a sequence of
monophonic music symbols corresponding to the notations present in the image.
The Residual Recurrent Convolutional block can improve the ability of the model
to enrich the context information. The experiment results are benchmarked
against a publicly available dataset called CAMERA-PRIMUS, which demonstrates
that our approach surpass the state-of-the-art end-to-end method using
Convolutional Recurrent Neural Network.
