- Tristan Repolusk ORCID:orcid.org/0009-0009-8435-118510 &
- Eduardo Veas ORCID:orcid.org/0000-0002-0356-403410
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14809))
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Abstract
In recent years, the development of Optical Music Recognition (OMR) has progressed significantly. However, music cultures with smaller communities have only recently been considered in this process. This results in a lack of adequate ground truth datasets needed for the development and benchmarking of OMR systems. In this work, the KuiSCIMA (Jiang Kui Score Images for Musicological Analysis) dataset is introduced. KuiSCIMA is the first machine-readable dataset of thesuzipu notations in Jiang Kui’s collectionBaishidaoren Gequ from 1202. Collected from five different woodblock print editions, the dataset contains 21797 manually annotated instances on 153 pages in total, from which 14500 are text character annotations, and 7297 aresuzipu notation symbols. The dataset comes with an open-source tool which allows editing, visualizing, and exporting the contents of the dataset files. In total, this contribution promotes the preservation and understanding of cultural heritage through digitization.
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Notes
- 1.
The abbreviation MUSCIMA stands for MUsic SCore IMAges, being the inspiration for the name of the KuiSCIMA dataset.
- 2.
- 3.
- 4.
https://github.com/SuziAI/SuziOMR/tree/main/baseline (taggedv1.0).
- 5.
The dataset extraction procedure is described inhttps://github.com/SuziAI/gui-tools/blob/main/readme_files/README_ANNOTATION_TOOL.md#extract-omr-dataset-from-corpus (taggedv2.0).
- 6.
https://github.com/SuziAI/KuiSCIMA (taggedv1.0).
- 7.
https://github.com/SuziAI/gui-tools (taggedv2.0).
- 8.
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Know-Center GmbH, Graz, Austria
Tristan Repolusk & Eduardo Veas
- Tristan Repolusk
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Luleå Tekniska Universitet, Luleå, Sweden
Elisa H. Barney Smith
Luleå Tekniska Universitet, Luleå, Sweden
Marcus Liwicki
Tsinghua University, Beijing, China
Liangrui Peng
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Repolusk, T., Veas, E. (2024). The KuiSCIMA Dataset for Optical Music Recognition of Ancient Chinese Suzipu Notation. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14809. Springer, Cham. https://doi.org/10.1007/978-3-031-70552-6_3
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