  The long-tail effect is a common issue that limits the performance of deep
learning models on real-world datasets. Character image datasets are also
affected by such unbalanced data distribution due to differences in character
usage frequency. Thus, current character recognition methods are limited when
applied in the real world, especially for the categories in the tail that lack
training samples, e.g., uncommon characters. In this paper, we propose a
zero-shot character recognition framework via radical-based reasoning, called
RZCR, to improve the recognition performance of few-sample character categories
in the tail. Specifically, we exploit radicals, the graphical units of
characters, by decomposing and reconstructing characters according to
orthography. RZCR consists of a visual semantic fusion-based radical
information extractor (RIE) and a knowledge graph character reasoner (KGR). RIE
aims to recognize candidate radicals and their possible structural relations
from character images in parallel. The results are then fed into KGR to
recognize the target character by reasoning with a knowledge graph. We validate
our method on multiple datasets, and RZCR shows promising experimental results,
especially on few-sample character datasets.
