  Recognition of Off-line Chinese characters is still a challenging problem,
especially in historical documents, not only in the number of classes extremely
large in comparison to contemporary image retrieval methods, but also new
unseen classes can be expected under open learning conditions (even for CNN).
Chinese character recognition with zero or a few training samples is a
difficult problem and has not been studied yet. In this paper, we propose a new
Chinese character recognition method by multi-type attributes, which are based
on pronunciation, structure and radicals of Chinese characters, applied to
character recognition in historical books. This intermediate attribute code has
a strong advantage over the common `one-hot' class representation because it
allows for understanding complex and unseen patterns symbolically using
attributes. First, each character is represented by four groups of attribute
types to cover a wide range of character possibilities: Pinyin label, layout
structure, number of strokes, three different input methods such as Cangjie,
Zhengma and Wubi, as well as a four-corner encoding method. A convolutional
neural network (CNN) is trained to learn these attributes. Subsequently,
characters can be easily recognized by these attributes using a distance metric
and a complete lexicon that is encoded in attribute space. We evaluate the
proposed method on two open data sets: printed Chinese character recognition
for zero-shot learning, historical characters for few-shot learning and a
closed set: handwritten Chinese characters. Experimental results show a good
general classification of seen classes but also a very promising generalization
ability to unseen characters.
