The MUSCIMA++ Dataset for Handwritten Optical Music Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10372147" target="_blank" >RIV/00216208:11320/17:10372147 - isvavai.cz</a>
Result on the web
<a href="http://doi.org/10.1109/ICDAR.2017.16" target="_blank" >http://doi.org/10.1109/ICDAR.2017.16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDAR.2017.16" target="_blank" >10.1109/ICDAR.2017.16</a>
Alternative languages
Result language
angličtina
Original language name
The MUSCIMA++ Dataset for Handwritten Optical Music Recognition
Original language description
Optical Music Recognition (OMR) promises to make accessible the content of large amounts of musical documents, an important component of cultural heritage. However, the field does not have an adequate dataset and ground truth for benchmarking OMR systems, which has been a major obstacle to measurable progress. Furthermore, machine learn- ing methods for OMR require training data. We design and collect MUSCIMA++, a new dataset for OMR. Ground truth in MUSCIMA++ is a notation graph, which our analysis shows to be a necessary and sufficient representation of music notation. Building on the CVC-MUSCIMA dataset for staffline removal, the MUSCIMA++ dataset v1.0 consists of 140 pages of hand- written music, with 91254 manually annotated notation symbols and 82247 explicitly marked relationships between symbol pairs. The dataset allows training and directly evaluating models for symbol classification, symbol localization, and notation graph assembly, and musical content extraction, both in isolation and joint
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/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
14th International Conference on Document Analysis and Recognition, ICDAR 2017, Kyoto, Japan, November 13 - 15, 2017
ISBN
978-1-5386-3586-5
ISSN
2379-2140
e-ISSN
neuvedeno
Number of pages
8
Pages from-to
39-46
Publisher name
IEEE Computer Society
Place of publication
New York, USA
Event location
Kyoto, Japan
Event date
Nov 13, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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