Full-page recognition and alignment of historical musical documents

[EN] Optical Music Recognition aims to transcribe musical manuscript images into digital formats by using automatic methods for enhanced accessibility and preservation. This task is challenging for handwritten historical musical pieces from the Late Middle Ages, Early Renaissance, and previous time...

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Detalles Bibliográficos
Autores: Villarreal-Ruiz, Manuel|||0000-0003-2889-9030, Sánchez Peiró, Joan Andreu|||0000-0003-0423-2020, Parres-Montoya, Daniel
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::89063202787514ec5610a6a9d3cf7d7c
Acceso en línea:https://riunet.upv.es/handle/10251/234594
Access Level:acceso abierto
Palabra clave:Optical Music Recognition
Handwritten Music Recognition
Transformer models
CRNN models
Aligned Music and Lyrics Transcription
Full Page Recognition
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spelling Full-page recognition and alignment of historical musical documentsVillarreal-Ruiz, Manuel|||0000-0003-2889-9030Sánchez Peiró, Joan Andreu|||0000-0003-0423-2020Parres-Montoya, DanielOptical Music RecognitionHandwritten Music RecognitionTransformer modelsCRNN modelsAligned Music and Lyrics TranscriptionFull Page Recognition[EN] Optical Music Recognition aims to transcribe musical manuscript images into digital formats by using automatic methods for enhanced accessibility and preservation. This task is challenging for handwritten historical musical pieces from the Late Middle Ages, Early Renaissance, and previous time periods. This music has the interesting characteristic that both musical and lyrical elements are present with an implicit time alignment between them. This paper introduces techniques for simultaneously transcribing the musical and lyrical elements. We research how to automatically obtain the time alignment for an accurate musicological interpretation. Convolutional and Recurrent Neural Networks and Transformer models are explored for holistically transcribing and aligning historical pieces. This paper explores different techniques to improve the training of the models in limited data scenarios. Experiments are conducted on two different datasets from the same time period. Our findings highlight the potential of Transformer models in overcoming the alignment challenge, providing the best alignment capabilities without compromising the quality of transcriptions and offering a promising direction for future research in the automatic recognition of historical musical documents.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: CRUE-Universitat Politècnica de València". This work is supported by Generalitat Valenciana GVA through Conselleria de Educación, Universidades y Empleo under project LightVED, funded by the PROMETEO 2024 program (CIPROM/2023/17), by Grant CIACIF/2021/287 funded by Generalitat Valenciana GVA and by Project PID2024-161104OB-C21 funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE. The authors would also like to thank Dr. Elisa Barney Smith for her valuable feedback and suggestions during the review of this manuscript.Springer-VerlagDepartamento de Sistemas Informáticos y ComputaciónEscuela Técnica Superior de Ingeniería InformáticaCentro de Investigación Pattern Recognition and Human Language TechnologyGeneralitat ValencianaConsejo Superior de Investigaciones CientíficasMinisterio de Ciencia, Innovación y UniversidadesRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/234594reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengGeneralitat Valenciana https://doi.org/10.13039/501100003359 CIPROM%2F2023%2F017Generalitat Valenciana https://doi.org/10.13039/501100003359 CIACIF%2F2021%2F287Ministerio de Ciencia, Innovación y Universidades https://doi.org/10.13039/100014440 PID2024-161104OB-C21open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::89063202787514ec5610a6a9d3cf7d7c2026-06-13T07:49:27Z
dc.title.none.fl_str_mv Full-page recognition and alignment of historical musical documents
title Full-page recognition and alignment of historical musical documents
spellingShingle Full-page recognition and alignment of historical musical documents
Villarreal-Ruiz, Manuel|||0000-0003-2889-9030
Optical Music Recognition
Handwritten Music Recognition
Transformer models
CRNN models
Aligned Music and Lyrics Transcription
Full Page Recognition
title_short Full-page recognition and alignment of historical musical documents
title_full Full-page recognition and alignment of historical musical documents
title_fullStr Full-page recognition and alignment of historical musical documents
title_full_unstemmed Full-page recognition and alignment of historical musical documents
title_sort Full-page recognition and alignment of historical musical documents
dc.creator.none.fl_str_mv Villarreal-Ruiz, Manuel|||0000-0003-2889-9030
Sánchez Peiró, Joan Andreu|||0000-0003-0423-2020
Parres-Montoya, Daniel
author Villarreal-Ruiz, Manuel|||0000-0003-2889-9030
author_facet Villarreal-Ruiz, Manuel|||0000-0003-2889-9030
Sánchez Peiró, Joan Andreu|||0000-0003-0423-2020
Parres-Montoya, Daniel
author_role author
author2 Sánchez Peiró, Joan Andreu|||0000-0003-0423-2020
Parres-Montoya, Daniel
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Sistemas Informáticos y Computación
Escuela Técnica Superior de Ingeniería Informática
Centro de Investigación Pattern Recognition and Human Language Technology
Generalitat Valenciana
Consejo Superior de Investigaciones Científicas
Ministerio de Ciencia, Innovación y Universidades
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Optical Music Recognition
Handwritten Music Recognition
Transformer models
CRNN models
Aligned Music and Lyrics Transcription
Full Page Recognition
topic Optical Music Recognition
Handwritten Music Recognition
Transformer models
CRNN models
Aligned Music and Lyrics Transcription
Full Page Recognition
description [EN] Optical Music Recognition aims to transcribe musical manuscript images into digital formats by using automatic methods for enhanced accessibility and preservation. This task is challenging for handwritten historical musical pieces from the Late Middle Ages, Early Renaissance, and previous time periods. This music has the interesting characteristic that both musical and lyrical elements are present with an implicit time alignment between them. This paper introduces techniques for simultaneously transcribing the musical and lyrical elements. We research how to automatically obtain the time alignment for an accurate musicological interpretation. Convolutional and Recurrent Neural Networks and Transformer models are explored for holistically transcribing and aligning historical pieces. This paper explores different techniques to improve the training of the models in limited data scenarios. Experiments are conducted on two different datasets from the same time period. Our findings highlight the potential of Transformer models in overcoming the alignment challenge, providing the best alignment capabilities without compromising the quality of transcriptions and offering a promising direction for future research in the automatic recognition of historical musical documents.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/234594
url https://riunet.upv.es/handle/10251/234594
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Generalitat Valenciana https://doi.org/10.13039/501100003359 CIPROM%2F2023%2F017
Generalitat Valenciana https://doi.org/10.13039/501100003359 CIACIF%2F2021%2F287
Ministerio de Ciencia, Innovación y Universidades https://doi.org/10.13039/100014440 PID2024-161104OB-C21
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer-Verlag
publisher.none.fl_str_mv Springer-Verlag
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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