Applications and Challenges of AI Technologies in the Preservation of Dongba Script and Naxi Cultural Heritage
DOI:
https://doi.org/10.62051/jk2hm924Keywords:
Artificial intelligence; Cultural heritage preservation; Naxi script; Dongba culture; Digital preservation; Large language models; Intangible cultural heritage.Abstract
Nowadays, cultural heritage preservation is currently under a serious global threat that around 20% of UNESCO heritages face a risk of disappearance. Naxi paper and Dongba script, an ancient hieroglyphic system, located in southwestern China, face the serious challenges of digital preservation and transmission. Moreover, there are two problems with current conservation methods. Firstly, the traditional oral transmission lacks permanence. Besides, the methods existing fail to combine multimedia such as text, audio and image together. This study will investigate how AI could boost and enable cultural transmission by using existing academic literature and documented AI applications including Deep Learning, OCR, Large Language models, and Generative AI models. These tools could digitize manuscripts, reconstruct damaged characters, and enable global dissemination. Acknowledging that using AI tools do have some limitations such as cultural ownership concerns, unusual digital access, and AI reliability. Beyond these three limitations, AI tools are potentially responsible for cultural heritage’s preservation.
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[1] Xu X, Li D, Jiang Z, Li N, Wu G, Wang H, Zhang X, Bai F, et al. Construction of an International Digital Sharing Platform of Dongba Manuscripts and Dongba Hieroglyphs. Computer Systems Science & Engineering, 2019, 34(4): 191-199. https://doi.org/10.32604/csse.2019.34.191 DOI: https://doi.org/10.32604/csse.2019.34.191
[2] Ba’ai N M, Aris A. AI and Cultural Heritage: Preserving and Promoting Global Cultures Through Technology. Nanotechnology Perceptions, 2024, 20(S15): 170-176. https://doi.org/10.62441/ nano-ntp.vi.3454 DOI: https://doi.org/10.62441/nano-ntp.vi.3454
[3] Fu Y, Shi K, Xi L. Artificial intelligence and machine learning in the preservation and innovation of intangible cultural heritage: ethical considerations and design frameworks. Digital Scholarship in the Humanities, 2025, 40(2): 487-508. https://doi.org/10.1093/llc/fqaf034 DOI: https://doi.org/10.1093/llc/fqaf034
[4] Li S, Liang Y, Han X. The application of artificial intelligence-assisted technology in cultural and creative product design. Scientific Reports, 2024, 14: 31069. https://doi.org/10.1038/s41598-024-82281-2 DOI: https://doi.org/10.1038/s41598-024-82281-2
[5] Sharofova A. The impact of AI on endangered languages: preservation efforts. Texas Journal of Philology, Culture and History, 2023, 25(1): 52-59. https://zienjournals.com/index.php/tjpch/ article/view/872
[6] Falk M T, Hagsten E. Intangible Cultural Heritage differently exposed across continents. npj Heritage Science, 2025, 13: 600. https://doi.org/10.1038/s40494-025-02169-w DOI: https://doi.org/10.1038/s40494-025-02169-w
[7] Ma Y, Li Y, Long G, et al. Dataset for Single Character Detection in Dongba Manuscripts. Scientific Data, 2025, 12: 1075. https://doi.org/10.1038/s41597-025-05434-6 DOI: https://doi.org/10.1038/s41597-025-05434-6
[8] Du J, Zhang X, Jiang K, Hu Y, Tang J, Liu J, Henian E. Cultural Heritage Preservation using Multimedia and AI. Proceedings of the 17th International Conference on Digital Preservation (iPRES 2021), 2021. https://www.digipres.org/publications/ipres/ipres-2021/cultural-heritage-preservation-using-multimedia-and-ai/
[9] Lai S, Tian Y, Zhang Q. The impact of AI-generated technologies-driven digital cultural heritage platforms on users’ offline cultural participation intentions. npj Heritage Science, 2025, 13: 574. https://doi.org/10.1038/s40494-025-02148-1 DOI: https://doi.org/10.1038/s40494-025-02148-1
[10] Jayanthi J, Maheswari P U. Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methods. Heritage Science, 2024, 12: 63. https://doi.org/10.1186/s40494-024-01169-6 DOI: https://doi.org/10.1186/s40494-024-01169-6
[11] Bi X, Luo Y. Incomplete handwritten Dongba character image recognition by multiscale feature restoration. Heritage Science, 2024, 12: 218. https://doi.org/10.1186/s40494-024-01329-8 DOI: https://doi.org/10.1186/s40494-024-01329-8
[12] Europeana Foundation, European Schoolnet (EUN). Digital cultural heritage in education – a tool to navigate difficult times. Europeana Pro, 2021-08-30. https://pro.europeana.eu/post/digital-cultural-heritage-in-education-a-tool-to-navigate-difficult-times
[13] Gordin S, Rusakov Y, Yates A, Shmidman A, Smith D A. CuReD: Deep learning optical character recognition for Cuneiform text editions and legacy materials. Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024), 2024: 130-140. https://doi.org/10.18653/v1/2024.ml4al-1.14 DOI: https://doi.org/10.18653/v1/2024.ml4al-1.14
[14] Sperlì G. A cultural heritage framework using a Deep Learning based Chatbot for supporting tourist journey. Expert Systems with Applications, 2021, 183: 115277. https://doi.org/10.1016/ j.eswa.2021.115277 DOI: https://doi.org/10.1016/j.eswa.2021.115277
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