Edge‐Channel Aggregation Network and Two‐Stage Fine Tuning Scheme for Handwritten Dongba Character Recognition

ABSTRACT Handwritten Dongba Character Recognition (HDCR) contains a large number of visually similar characters with subtle and fragile edge cues, posing severe challenges to feature learning. To address this issue, an Edge Channel Aggregation Network (EdgeCANet) model is proposed. The core of EdgeCANet is the Edge Channel Attention Aggregation (ECAA) block, which integrates the Edge‐Guided Enhancement Module (EGEM) for edge‐aware spatial refinement and the Channel Texture Recalibration Module (CTRM) for channel‐wise texture recalibration. Besides, the two‐stage misclassified sample weight redistribution fine‐tuning scheme based on Exponential Moving Average (EMA) algorithm is proposed to stabilise parameter updates, and to emphasise difficult and highly similar classes. The recognition accuracy of EdgeCANet significantly outperforms state‐of‐the‐art models across the constructed high‐similarity datasets HS‐DB and HS‐S20 K, as well as the public DB1404, OBC306, Sketch‐20 K, and ImageNet‐Sketch datasets. The results validate that EdgeCANet effectively enhances recognition accuracy for highly similar handwritten characters and ancient minority‐script characters, offering more promising applicability for related recognition tasks.