Improving Chinese Text Recognition with Multi-Granularity Features and Vision-Language Reasoning

Accurate Chinese text recognition (CTR) is vital for applications such as document digitization, but remains challenging due to high inter-class visual similarity, complex hierarchical structures, and diverse visual degradations in real-world scenes. To address these challenges, we propose a robust CTR framework that synergizes structure-aware visual discrimination with cross-modal reasoning. The hybrid encoder integrates multi-scale attention modulation and global self-attention, guided by hierarchical structural supervision, to capture fine-grained structure-aware visual representations essential for distinguishing visually similar characters. Complementing this, an iterative vision-language decoder, trained with a stochastic masking strategy, learns to reconstruct text from partially observed visual and contextual cues, enabling complementary vision-language reasoning that effectively resolves visually ambiguous or degraded characters. Extensive experiments on public benchmarks demonstrate that our method achieves state-of-the-art performance, validating its effectiveness in addressing the challenges of Chinese text recognition.