Radical aggregation network for few-shot offline handwritten Chinese character recognition
Tianwei Wang, Zecheng Xie, Zhe Li, Lianwen Jin, Xiangle Chen · Pattern Recognition Letters · 2019
Offline handwritten Chinese character recognition has attracted much interest due to its various applications. The most cutting-edge methods treat Chinese character as a whole, ignoring the structures and radicals that compose characters. To use the radical-level composition of Chinese characters and achieve few-shot/zero-shot Chinese character recognition, some methods attempt to recognize Chinese characters at the radical level; however, these methods have shown poor performance due to weak radical feature representation and the use of inflexible decoding algorithm. In this paper, a novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition. The RAN consists of three components, a radical mapping encoder (RME), a radical aggregation module (RAM), and a character analysis decoder (CAD). Experiments show that our method can effectively recognize unseen handwritten characters given few support samples, while maintaining a high performance on seen characters.