RadSimLoss: Radical Similarity-Guided Loss for Zero-Shot Chinese Character Recognition

Zero-shot Chinese character recognition is an important topic in the signal processing field. Existing glyph-based methods achieve competitive performance by considering glyph similarity, but they incur substantial computational overhead due to the need for a visual encoder. Recognition methods based on ideographic description sequences (IDS) are more efficient, yet they do not account for inter-character similarity during representation learning, which may limit recognition performance. To alleviate this limitation, we propose a radical similarity–guided loss (RadSimLoss) for zero-shot Chinese character recognition. The RadSimLoss computes pairwise similarities between characters from their IDS, replacing glyph-based similarity, and incorporates them into the training objective. Experimental results on handwritten, scene, printed artistic, and ancient Chinese character datasets show that our proposed RadSimLoss is effective and outperforms a series of state-of-the-art approaches.