  The recognition of Chinese characters has always been a challenging task due
to their huge variety and complex structures. The latest research proves that
such an enormous character set can be decomposed into a collection of about 500
fundamental Chinese radicals, and based on which this problem can be solved
effectively. While with the constant advent of novel Chinese characters, the
number of basic radicals is also expanding. The current methods that entirely
rely on existing radicals are not flexible for identifying these novel
characters and fail to recognize these Chinese characters without learning all
of their radicals in the training stage. To this end, this paper proposes a
novel Hippocampus-heuristic Character Recognition Network (HCRN), which
references the way of hippocampus thinking, and can recognize unseen Chinese
characters (namely zero-shot learning) only by training part of radicals. More
specifically, the network architecture of HCRN is a new pseudo-siamese network
designed by us, which can learn features from pairs of input training character
samples and use them to predict unseen Chinese characters. The experimental
results show that HCRN is robust and effective. It can accurately predict about
16,330 unseen testing Chinese characters relied on only 500 trained Chinese
characters. The recognition accuracy of HCRN outperforms the state-of-the-art
Chinese radical recognition approach by 15% (from 85.1% to 99.9%) for
recognizing unseen Chinese characters.
