Improving CTC-based Handwritten Chinese Text Recognition with Cross-Modality Knowledge Distillation and Feature Aggregation

Offline handwritten Chinese text recognition (HCTR) models based on connectionist temporal classification (CTC) have recently achieved impressive results. Due to the conditional independence assumption and per-frame prediction characteristics, CTC-based models cannot capture semantic relationships between output tokens and leverage global visual features of characters. To solve these issues, we propose a Cross-Modality knowledge distillation approach that leverages pretrained LM (BERT) to transfer contextual semantic information, and then design a feature aggregation module to dynamically aggregate local and global features. Experimental results on the HCTR datasets (CASIA-HWDB, ICDAR2013, HCCDOC) show that our proposed method can significantly improve the model's performance.