DongbaBPN: Dongba Character Detection Based on Boundary GCN

Dongba script is a unique primitive pictographic writing system. Due to its complex layout structure and highly variable glyph shapes, detecting Dongba characters poses a significant challenge. To address this, we have established a high-quality Dongba script dataset, Dongba1800, which comprehensively covers various layout structures and spatial features found in ancient Dongba manuscripts. We also propose a character detection model named DongbaBPN, capable of precisely locating and detecting Dongba characters at the character level in complex pages. Specifically, our method directly models the boundaries of Dongba characters and consists of a feature extraction backbone similar to an FPN, an initial boundary proposal module, and a GCN-based boundary iterative refinement module. The boundary proposal module generates initial Dongba character boundary proposals by extracting feature semantic information, and then the boundary iterative refinement module progressively refines these proposals until the boundaries can accurately locate and cover individual Dongba characters. Additionally, we conducted comparative experiments with other state-of-the-art text detection models, and our method achieved the best performance in the Dongba character detection task.