Synthesizing Training Data for Handwritten Music Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440561" target="_blank" >RIV/00216208:11320/21:10440561 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-86334-0.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-3-030-86334-0.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-86334-0_41" target="_blank" >10.1007/978-3-030-86334-0_41</a>
Alternative languages
Result language
angličtina
Original language name
Synthesizing Training Data for Handwritten Music Recognition
Original language description
Handwritten music recognition is a challenging task that could be of great use if mastered, e.g., to improve the accessibility of archival manuscripts or to ease music composition. Many modern machine learning techniques, however, cannot be easily applied to this task because of the limited availability of high-quality training data. Annotating such data manually is expensive and thus not feasible at the necessary scale. This problem has already been tackled in other fields by training on automatically generated synthetic data. We bring this approach to handwritten music recognition and present a method to generate synthetic handwritten music images (limited to monophonic scores) and show that training on such data leads to state-of-the-art results.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
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/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
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 2021
ISBN
978-3-030-86333-3
ISSN
—
e-ISSN
—
Number of pages
16
Pages from-to
626-641
Publisher name
Springer International Publishing
Place of publication
Cham, Switzerland
Event location
Lausanne, Switzerland
Event date
Sep 5, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—