Building Hierarchical Representations for Oracle Character and Sketch Recognition

In this paper, we study oracle character recognition
and general sketch recognition. First, a data set of oracle
characters, which are the oldest hieroglyphs in China yet remain
a part of modern Chinese characters, is collected for analysis.
Second, typical visual representations in shape- and sketchrelated
works are evaluated. We analyze the problems suffered
when addressing these representations and determine several
representation design criteria. Based on the analysis, we propose
a novel hierarchical representation that combines a Gabor-related
low-level representation and a sparse-encoder-related mid-level
representation. Extensive experiments show the effectiveness of
the proposed representation in both oracle character recognition
and general sketch recognition. The proposed representation
is also complementary to convolutional neural network (CNN)-
based models. We introduce a solution to combine the
proposed representation with CNN-based models, and achieve
better performances over both approaches. This solution has
beaten humans at recognizing general sketches.