TY - GEN
T1 - The KuiSCIMA Dataset for Optical Music Recognition of Ancient Chinese Suzipu Notation
AU - Repolusk, Tristan
AU - Veas, Eduardo Enrique
PY - 2024/9/11
Y1 - 2024/9/11
N2 - 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 the suzipu notations in Jiang Kui’s collection Baishidaoren 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 are suzipu 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.
AB - 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 the suzipu notations in Jiang Kui’s collection Baishidaoren 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 are suzipu 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.
KW - optical music recognition
KW - Cultural heritage
KW - Optical Music Recognition
KW - Jiang Kui
KW - Suzipu
KW - Ancient Chinese music
KW - Banzipu
UR - http://www.scopus.com/inward/record.url?scp=85204513642&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70552-6_3
DO - 10.1007/978-3-031-70552-6_3
M3 - Conference paper
SN - 9783031705519
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 38
EP - 54
BT - Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Proceedings
A2 - Barney Smith, Elisa H.
A2 - Liwicki, Marcus
A2 - Peng, Liangrui
PB - Springer Nature Switzerland AG
ER -