Self-information of radicals: A new clue for zero-shot Chinese character recognition

Guo-Feng Luo, Da-Han Wang, Xia Du, Huayi Yin, Xu-Yao Zhang, Shunzhi Zhu

Abstract: Highlights•We propose the self-information of radicals (SIR) from the information theory perspective to measure the importance of radicals in recognizing Chinese characters.•The proposed SIR can be easily adopted by two commonly used radical-based zero-shot Chinese Character Recognition (ZSCCR) frameworks, i.e., sequence matching based and attribute embedding based.•For sequence matching based ZSCCR, we propose a novel Chinese character uncertainty elimination (CUE) framework, which is capable of alleviating the sequence mismatch problem.•For attribute embedding based ZSCCR, we propose a novel radical information embedding (RIE) method, which can highlight the importance of indispensable radicals.•Comprehensive experiments on the CASIA-HWDB, ICDAR2013, CTW, and AHCDB datasets demonstrate the effectiveness and high extensibility of the proposed SIR.