RRecT: Chinese Text Recognition with Radical-Enhanced Recognition Transformer

Text recognition has attracted continuous attention in recent years and reaches increasingly high performance on English datasets. Nevertheless, little attention is emphatically paid to Chinese Text Recognition (CTR), leading to a barely satisfying accuracy on Chinese datasets. Due to the complex glyph structure and large size of character set, CTR is more challenging and requires a powerful capacity of feature extraction. In this paper, we propose a novel network for CTR named Radical-enhanced Recognition Transformer (RRecT). It firstly introduces a customized Recognition Transformer (RecT) to extract multi-grained features, then exploits radical decomposition as an auxiliary supervision signal and enhances character representation with radical information by Radical Prediction Module (RPM) and Radical-Character Fusion Module (RCFM). Thus, final feature contains both character-level and fused radical-level information. The experimental results show that RRecT outperforms the state-of-the-art methods by a margin of 1.4% on Scene dataset, 1.8% on Document dataset and reaches a competitive performance on Web and Handwritten dataset. Moreover, RRecT requires much less computation cost and is a lightweight and effective model.