The MUSCIMA++ Dataset for Handwritten Optical Music Recognition

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

The MUSCIMA++ Dataset for Handwritten Optical Music Recognition

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

The MUSCIMA++ Dataset for Handwritten Optical Music Recognition

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

8

Strana od-do

39-46

Název nakladatele

IEEE Computer Society

Místo vydání

New York, USA

Místo konání akce

Kyoto, Japan

Datum konání akce

13. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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