From Global Alignment to Local Grounding: Zero-Shot Chinese Character Recognition with Radical Verification
This study addresses the limitations of zero-shot Chinese character recognition, where global matching overlooks the spatial layout of radicals and fine-grained ranking fails to capture subtle differences. To this end, this work proposes a global-to-local two-stage framework. In the retrieval stage, building upon a CLIP architecture with Ideographic Description Sequence (IDS) encoding, spatially aware prototypes are constructed by incorporating explicit tree positions and radical-level geometric priors, enabling high-recall candidate retrieval. In the re-ranking stage, a margin-gated radical verification mechanism is designed to enhance local discriminability through instance-query matching, thereby achieving precise ranking. The proposed method attains a Top-1 accuracy of 83.06% on the ICDAR2013 benchmark, establishing state-of-the-art performance. Comprehensive ablation studies further validate the effectiveness of each individual module within the framework.