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

Autor: Carlos Garrido-Munoz, Antonio Rios-Vila, Jorge Calvo-Zaragoza
Přispěvatelé: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante. Instituto Universitario de Investigación Informática, Reconocimiento de Formas e Inteligencia Artificial
Rok vydání: 2022
Předmět:
Zdroj: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
ISSN: 1433-2825
1433-2833
DOI: 10.1007/s10032-022-00417-4
Popis: 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. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Work produced with the support of a 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. The Foundation takes no responsibility for the opinions, statements and contents of this project, which are entirely the responsibility of its authors. The second author is supported by grant ACIF/2021/356 from the “Programa I+D+i de la Generalitat Valenciana”.
Databáze: OpenAIRE
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