Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference

TL;DR

This paper introduces a novel hierarchical network for zero-shot Chinese character recognition that combines global and local representations, improving accuracy and efficiency in open-world scenarios.

Contribution

The proposed GL-HPN model jointly learns global and local features with a hierarchical inference strategy, addressing limitations of existing holistic approaches.

Findings

Achieves competitive zero-shot recognition performance.

Excels in low-resource settings.

Reduces inference cost for large candidate sets.

Abstract

Chinese character categories are extremely large, and unseen characters frequently arise in open-world scenarios, making zero-shot Chinese character recognition an important yet challenging problem. Existing IDS-based retrieval methods usually encode a character image and its ideographic description sequence into a single global vector for matching. Although efficient, such holistic alignment often under-models local component differences. Moreover, directly introducing patch-token level fine-grained interaction suffers from both the noise of structural operators in IDS and the high cost of full-candidate retrieval.To address these issues, we propose a Global-Local Hierarchical Perception Network (GL-HPN), which jointly learns global and local representations of character images and IDS sequences within a unified cross-modal alignment framework. The global branch supports efficient coarse recall, while the local branch improves component-level discrimination through patch-token interaction. We further introduce a structure filtering mask to suppress structurally meaningful but visually non-entity IDS operators in local similarity aggregation. On top of this, we design a coarse-to-fine hierarchical inference strategy that performs global retrieval over the full candidate set and local reranking only on Top-$K$ candidates, followed by parameter-free multiplicative fusion of normalized posterior scores. Experimental results show that GL-HPN achieves competitive performance across multiple zero-shot splits, performs especially well under low-resource settings, and substantially reduces the inference cost of large-scale candidate retrieval.