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

• 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. Zero-shot Chinese character recognition (ZSCCR) is an important research topic in Chinese character recognition as it attempts to recognize unseen Chinese characters. As basic components and mid-level representations, radicals are significant for ZSCCR. However, previous methods treat the importance of radicals equally, ignoring the different contributions of radicals in distinguishing characters. In this paper, we propose the self-information of radicals (SIR) to measure the importance of radicals in recognizing Chinese characters. The proposed SIR can be easily adopted by two commonly used radical-based 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 to alleviate the radical sequence mismatch problem. For attribute embedding based ZSCCR, we propose a novel radical information embedding (RIE) method that can highlight the importance of indispensable radicals and weaken the influence of some unnecessary radicals. We conducted comprehensive experiments on the CASIA-HWDB, ICDAR2013, CTW datasets, and AHCDB datasets to evaluate the proposed method. Experiments show that our proposed methods can achieve superior performance to the state-of-the-art methods, which demonstrate the effectiveness and the high extensibility of the proposed SIR. [ABSTRACT FROM AUTHOR]