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The MUSCIMA++ Dataset for Handwritten Optical Music Recognition

Authors :
Jan Hajič
Pavel Pecina
Source :
ICDAR
Publication Year :
2017
Publisher :
IEEE, 2017.

Abstract

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 learning 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 handwritten 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 indirectly musical content extraction, both in isolation and jointly. Open-source tools are provided for manipulating the dataset, visualizing the data and annotating further, and the data is made available under an open license.

Details

Database :
OpenAIRE
Journal :
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Accession number :
edsair.doi...........2d9902878b09099678035609c87a7bba
Full Text :
https://doi.org/10.1109/icdar.2017.16