A holistic approach for image-to-graph: application to optical music recognition

Abstract

A number of applications would benefit from neural approaches that are capable of generating graphs from images in an end-to-end fashion. One of these fields is optical music recognition (OMR), which focuses on the computational reading of music notation from document images. Given that music notation can be expressed as a graph, the aforementioned approach represents a promising solution for OMR. In this work, we propose a new neural architecture that retrieves a certain representation of a graph—identified by a specific order of its vertices—in an end-to-end manner. This architecture works by means of a double output: It sequentially predicts the possible categories of the vertices, along with the edges between each of their pairs. The experiments carried out prove the effectiveness of our proposal as regards retrieving graph structures from excerpts of handwritten musical notation. Our results also show that certain design decisions, such as the choice of graph representations, play a fundamental role in the performance of this approach.