Practical End-to-End Optical Music Recognition for Pianoform Music
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492884" target="_blank" >RIV/00216208:11320/24:10492884 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-70552-6_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-70552-6_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-70552-6_4" target="_blank" >10.1007/978-3-031-70552-6_4</a>
Alternative languages
Result language
angličtina
Original language name
Practical End-to-End Optical Music Recognition for Pianoform Music
Original language description
The majority of recent progress in Optical Music Recognition (OMR) has been achieved with Deep Learning methods, especially models following the end-to-end paradigm, reading input images and producing a linear sequence of tokens. Unfortunately, many music scores, especially piano music, cannot be easily converted to a linear sequence. This has led OMR researchers to use custom linearized encodings, instead of broadly accepted structured formats for music notation. Their diversity makes it difficult to compare the performance of OMR systems directly. To bring recent OMR model progress closer to useful results: (a) We define a sequential format called Linearized MusicXML, allowing to train an end-to-end model directly and maintaining close cohesion and compatibility with the industry-standard MusicXML format. (b) We create a dev and test set for benchmarking typeset OMR with MusicXML ground truth based on the OpenScore Lieder corpus. They contain 1,438 and 1,493 pianoform systems, each with an image fro
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/DH23P03OVV008" target="_blank" >DH23P03OVV008: OmniOMR - optical music recognition using machine learning for digital libraries</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Document Analysis and Recognition -- ICDAR 2024
ISBN
978-3-031-70551-9
ISSN
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e-ISSN
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Number of pages
19
Pages from-to
55-73
Publisher name
Springer International Publishing
Place of publication
Cham, Switzerland
Event location
Athîna, Greece
Event date
Aug 30, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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