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The Dongba script, a unique pictographic writing system invented by the ancestors of the Naxi people of China, holds great significance for interpreting the ancient Naxi language, history, and culture. Accurate detection of the Dongba script is crucial for in-depth research into Dongba manuscripts. Through automated Dongba script detection technology, experts can efficiently extract precise text data from many manuscripts, providing essential support for the subsequent translation of Dongba script and constructing a corpus. In response to this need, we have developed the 'Dongba1800' dataset, designed explicitly for Dongba script detection. This dataset comprises 1800 annotated images of Dongba manuscripts, with resolutions ranging from 1200 × 416 to 1201 × 530, totaling 111,702 Dongba characters. The characteristics of Dongba character images include (1) the complexity of Dongba characters, with varying sizes and nonlinear arrangements; (2) significant differences in writing styles among different Dongba scribes; and (3) severe aging and noise caused by long-term use and preservation. The Dongba1800 dataset provides a powerful tool for archaeologists, greatly simplifying and optimizing the organization and study of Dongba manuscripts. Additionally, we have conducted technical validations with various text detection models on the Dongba1800 dataset to ensure its effectiveness and reliability.
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http://dx.doi.org/10.1038/s41597-025-05434-6 | DOI Listing |
Sci Data
July 2025
College of Computer Information Science, College of Software, Southwest University, Chongqing, 400715, China.
The Dongba script, a unique pictographic writing system invented by the ancestors of the Naxi people of China, holds great significance for interpreting the ancient Naxi language, history, and culture. Accurate detection of the Dongba script is crucial for in-depth research into Dongba manuscripts. Through automated Dongba script detection technology, experts can efficiently extract precise text data from many manuscripts, providing essential support for the subsequent translation of Dongba script and constructing a corpus.
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May 2024
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Dongba characters are ancient ideographic scripts with abstract expressions that differ greatly from modern Chinese characters; directly applying existing methods cannot achieve the font style transfer of Dongba characters. This paper proposes an Attention-based Font style transfer Generative Adversarial Network (AFGAN) method. Based on the characteristics of Dongba character images, two core modules are set up in the proposed AFGAN, namely void constraint and font stroke constraint.
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