{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T08:12:05Z","timestamp":1768291925806,"version":"3.49.0"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            The\n            <jats:italic toggle=\"yes\">Jeongganbo<\/jats:italic>\n            notation, the first music representation system in East Asia capable of jointly expressing pitch and duration, has been extensively used\u2014and still is\u2014in the Korean music tradition since its inception in the 15th century. In this regard, there exists a plethora of music works that exclusively endure as physical sheets, which not only constitutes a heritage preservation challenge due to the inherent degradation of this format but also impedes the use of computational tools to study and exploit this music tradition. While the Optical Music Recognition (OMR) field, which represents the research area devoted to devising methods capable of automatically transcribing music sheets into digital formats, has addressed this issue in a number of music notations from the Western tradition, no previous research has considered the preservation of Jeongganbo scores. In this context, this work presents the following contributions: (i) the first data assortment of real Jeongganbo scores for OMR tasks; (ii) a collection of synthetic data generation and augmentation mechanisms to alleviate the scarcity of manual annotation; and (iii) a neural-based transcription scheme based on state-of-the-art OMR strategies specifically tailored to Jeongganbo scores. The experiments performed prove the validity of the approach\u2014performance rates close to a 90% of success\u2014and open new research avenues for under-resourced yet challenging music notations.\n          <\/jats:p>","DOI":"10.1145\/3715159","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:05:36Z","timestamp":1742990736000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["On the Automatic Recognition of Jeongganbo Music Notation: Dataset and Approach"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5445-7332","authenticated-orcid":false,"given":"Dongmin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Sogang University, Seoul, the Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1048-5466","authenticated-orcid":false,"given":"Danbinaerin","family":"Han","sequence":"additional","affiliation":[{"name":"Graduate School of Culture Technology, KAIST, Daejeon, the Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3655-1181","authenticated-orcid":false,"given":"Dasaem","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Art and Technology, Sogang University, Seoul, the Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8667-4070","authenticated-orcid":false,"given":"Jose J.","family":"Valero-Mas","sequence":"additional","affiliation":[{"name":"Pattern Recognition and Artificial Intelligence Group, University of Alicante, Spain"}]}],"member":"320","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-023-00278-5"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/app11083621"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.02.029"},{"key":"e_1_3_3_5_2","doi-asserted-by":"crossref","unstructured":"Arnau Bar\u00f3 Carles Badal and Alicia Forn\u00e9s. 2020. Handwritten historical music recognition by sequence-to-sequence with attention mechanism. In Proceedings of the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) 205\u2013210.","DOI":"10.1109\/ICFHR2020.2020.00046"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397499"},{"key":"e_1_3_3_7_2","doi-asserted-by":"crossref","unstructured":"Jorge Calvo-Zaragoza Juan C. Martinez-Sevilla Carlos Penarrubia and Antonio Rios-Vila. 2023. Optical music recognition: Recent advances current challenges and future directions. In Proceedings of the Document Analysis and Recognition workshop\u2014International Conference on Document Analysis and Recognition 94\u2013104.","DOI":"10.1007\/978-3-031-41498-5_7"},{"key":"e_1_3_3_8_2","first-page":"248","volume-title":"Proceedings of the 19th ISMIR Conference","author":"Calvo-Zaragoza Jorge","year":"2018","unstructured":"Jorge Calvo-Zaragoza and David Rizo. 2018. 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