Abstract Yi character detection in historical documents is challenged by complex morphology, dense strokes, and multi-scale layouts. To address these issues, we propose a novel fine-grained representation learning framework for Yi character detection (FGRL-YiNet) that integrates dynamic convolution and adaptive multi-scale fusion modules. This design enables the model to adaptively refine receptive fields to capture elusive stroke topology while suppressing background interference, directly addressing the fundamental limitations of static feature extraction in existing methods. Integrated with multi-scale feature fusion and a differentiable binarization head, our end-to-end system achieves robust character localization under severe degradation. Furthermore, we develop the YiPrint-694 dataset to support training in this low-resource domain. Extensive experiments show that FGRL-YiNet significantly outperforms state-of-the-art models on Yi benchmarks, particularly for weak strokes, and demonstrates strong generalizability on the public MTHv2 dataset. This work establishes a benchmark and architectural paradigm for underserved scripts, enabling practical solutions for digital heritage preservation.
Sun et al. (2026) studied this question.