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The KuiSCIMA Dataset for Optical Music Recognition of Ancient Chinese Suzipu Notation

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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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References

  1. Berten, O.: GregoBase: A database of Gregorian scores (2013).https://gregobase.selapa.net

  2. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools (2000)

    Google Scholar 

  3. Calvo-Zaragoza, J., Toselli, A.H., Vidal, E.: Handwritten music recognition for mensural notation with convolutional recurrent neural networks. Pattern Recogn. Lett.128 (2019).https://doi.org/10.1016/j.patrec.2019.08.021

  4. Chen, G.-F., Sheu, J.-S.: An optical music recognition system for traditional Chinese Kunqu Opera scores written in Gong-Che Notation. EURASIP J. Audio Speech Music Process. pp. 7–17.https://doi.org/10.1186/1687-4722-2014-7

  5. Cheng, H., et al.: SCUT-CAB: a new benchmark dataset of ancient chinese books with complex layouts for document layout analysis, November 2022, pp. 436–451. ISBN 978-3-031-21647-3.https://doi.org/10.1007/978-3-031-21648-0_30

  6. Cheng, Y.: Xi’an Guyue Xi’an old music in new China. ‘Living fossil’ or ‘flowing river’? Dissertation. School of Oriental and African Studies, University of London (2005).https://eprints.soas.ac.uk/29336/ 1/10731431.pdf. Accessed 03 Aug 2023

  7. Fornés, A., et al.: CVC-MUSCIMA: a ground-truth of handwritten music score images for writer identification and staff removal. Int. J. Doc. Anal. Recogn.15(3), 243–251 (2012).https://doi.org/10.1007/s10032-011-0168-2.

  8. Haji Jr., J., Pecina, P.: The MUSCIMA++ dataset for handwritten optical music recognition. In: 14th International Conference on Document Analysis and Recognition. ICDAR 2017, Kyoto, Japan, pp. 39–46 (2017)

    Google Scholar 

  9. Joshi, P.: Fashion mNIST with Pytorch (93% accuracy) (2019).https://www.kaggle.com/code/pankajj/fashion-mnist-with-pytorch-93-accuracy. Accessed 10 Feb 2024

  10. Lam, J.S.C.: Ci songs from the song dynasty: a Ménage à Trois of lyrics, music, and performance. New Liter. Hist.46(4), 623–646. (2015). ISSN 00286087, 1080661X.http://www.jstor.org/stable/24772762. Accessed 2 Aug 2023

  11. Ma, W., et al.: Joint layout analysis, character detection and recognition for historical document digitization. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 31– 36 (2020).https://doi.org/10.1109/ICFHR2020.2020.00017

  12. Martinez-Sevilla, J.C., et al.: On the performance of optical music recognition in the absence of specific training data. In: Proceedings of the 24th International Society for Music Information Retrieval Conference (Milan, Italy). ISMIR, November 2023, pp. 319–326 (2023).https://doi.org/ 10.5281/zenodo.10265289

  13. Repolusk, T., Veas, E.: The Suzipu musical annotation tool for the creation of machine-readable datasets of ancient Chinese music. In: Calvo-Zaragoza, J., Pacha, A., Shatri, E. (eds.) Proceedings of the 5th International Workshop on Reading Music Systems, Milan, Italy, pp. 7–11 (2023).https://doi.org/10.48550/arXiv.2311.04091.https://sites.google.com/view/worms2023/proceedings

  14. Saini, R., et al.: ICDAR 2019 historical document reading challenge on large structured chinese family records. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1499–1504 (2019).https://doi.org/10.1109/ICDAR.2019.00241

  15. Shen, T., et al.: Semantic recognition of common musical notes in Guqin score based on optimal statistical features. In: 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), pp. 1–4 (2022).https://doi.org/10.1109/CTISC54888.2022.9849792

  16. Sturgeon, D.: Chinese Text Project (2011).https://ctext.org/library.pl. Accessed 30 June 2023

  17. Sturgeon, D.: Large-scale optical character recognition of pre-modern Chinese texts. Int. J. Buddhist Thought Cult.28(2), 11–44 (2018)

    Google Scholar 

  18. Tang, C.-W., Liu, C.-L., Chiu, P.-S.: HRCenterNet: an anchorless approach to Chinese character segmentation in historical documents. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1924–1930 (2020)

    Google Scholar 

  19. West, A.C.: Musical notation for flute in Tangut manuscripts. In: Popova, I. (ed.) Tanguty v Central’noj Azii, pp. 443–454. Vostonaja literatura, Moskva (2012)

    Google Scholar 

  20. Yang, H., et al.: Dense and tight detection of Chinese characters in historical documents: datasets and a recognition guided detector. IEEE Access6, 30174–30183 (2018).https://doi.org/10.1109/ACCESS.2018.2840218

    Article  Google Scholar 

  21. Yang, Y.: Plum blossom on the far side of the stream. The renaissance of Jiang Kui’s Lyric Oeuvre with facsimiles and a new critical edition of the songs of the Whitestone Daoist. Hong Kong University Press, Hong Kong (2019)

    Google Scholar 

  22. Wu, S.. Songci Yinyue Zhuanti Yanjiu. Dissertation. Yangzhou University (2013)

    Google Scholar 

  23. Kui, J.: Baishidaoren Gequ. (Ed. by, Zumou, Z.). Guian: Zhushi (1913)

    Google Scholar 

  24. Kui, J.,. Baishidaoren Gequ. (Ed. by, Lu, Z.). reprinted in [16], [1743] (2011).https://ctext.org/library.pl?if=en&res=775747. Accessed 30 June 2023

  25. Kui, J.. Baishidaoren Gequ. (Ed. by, Zhang, Y). reprinted in [21], pp. 259–323, [1749] (2019)

    Google Scholar 

  26. Kui, J.. Baishidaoren Gequ. (Ed. by, Lu, Z., Min, H., Wang, Z.) reprinted in [21], pp. 193–254, [c.1736] (2019)

    Google Scholar 

  27. Kui J.,. Baishidaoren Gequ. In: Siku Quanshu, vol. 1. reprinted in [16].https://ctext.org/library.pl?res=106386. Accessed 30 June 2023

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Authors and Affiliations

  1. Know-Center GmbH, Graz, Austria

    Tristan Repolusk & Eduardo Veas

Authors
  1. Tristan Repolusk

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  2. Eduardo Veas

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Correspondence toTristan Repolusk.

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Editors and Affiliations

  1. Luleå Tekniska Universitet, Luleå, Sweden

    Elisa H. Barney Smith

  2. Luleå Tekniska Universitet, Luleå, Sweden

    Marcus Liwicki

  3. 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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