![]() The experiments carried out prove the effectiveness of our proposal as regards retrieving graph structures from excerpts of handwritten musical notation. 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. 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. Given that music notation can be expressed as a graph, the aforementioned approach represents a promising solution for OMR. One of these fields is optical music recognition (OMR), which focuses on the computational reading of music notation from document images. ![]() ![]() ![]() Abstract: Abstract A number of applications would benefit from neural approaches that are capable of generating graphs from images in an end-to-end fashion. ![]()
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