A novel cross-modal alignment learning framework for Dongba single-character dataset construction

The Dongba script is an ancient and unique pictographic writing system created by the Naxi people of China. Currently, existing datasets for Dongba character recognition, constructed through manual imitation or data augmentation, exhibit significant feature differences from authentic characters in ancient manuscripts, greatly limiting real-world application. To address this, we propose a novel dataset construction method based on cross-modal alignment learning for Dongba characters. Combined with dynamic anchor expansion retrieval and multi-granularity hybrid iterative training, we construct an authentic Dongba single-character dataset, Dongba_1512, comprising 1,512 categories and 705,058 samples. Extensive experiments demonstrate the effectiveness of both our proposed dataset construction method and the Dongba_1512, supporting digital research on Dongba manuscripts and showing superior transferability to other ancient scripts.