  Chinese characters have a huge set of character categories, more than 20,000
and the number is still increasing as more and more novel characters continue
being created. However, the enormous characters can be decomposed into a
compact set of about 500 fundamental and structural radicals. This paper
introduces a novel radical analysis network (RAN) to recognize printed Chinese
characters by identifying radicals and analyzing two-dimensional spatial
structures among them. The proposed RAN first extracts visual features from
input by employing convolutional neural networks as an encoder. Then a decoder
based on recurrent neural networks is employed, aiming at generating captions
of Chinese characters by detecting radicals and two-dimensional structures
through a spatial attention mechanism. The manner of treating a Chinese
character as a composition of radicals rather than a single character class
largely reduces the size of vocabulary and enables RAN to possess the ability
of recognizing unseen Chinese character classes, namely zero-shot learning.
