End-to-End Graph Prediction for Optical Music Recognition

Abstract

Modern advances in computer reading technologies have found excellent results using end-to-end neural approaches. In addition to reducing the number of steps required, these formulations force the neural network to learn to take into account the contextual nature of visual languages to boost recognition. So far, however, such approaches are only feasible when the output can be represented as a sequence. This works for many old music notations, or even for modern monophonic scores, but it is insufficient for the highest degrees of complexity of music notation. Furthermore, music notation can be nicely represented using a graph structure. In this paper, we present a first attempt at an end-to-end approach to predict graphs from images, taking complex music-notation symbols as input.