  Traditional approaches for handwritten Chinese character recognition suffer
in classifying similar characters. In this paper, we propose to discriminate
similar handwritten Chinese characters by using weakly supervised learning. Our
approach learns a discriminative SVM for each similar pair which simultaneously
localizes the discriminative region of similar character and makes the
classification. For the first time, similar handwritten Chinese character
recognition (SHCCR) is formulated as an optimization problem extended from SVM.
We also propose a novel feature descriptor, Gradient Context, and apply
bag-of-words model to represent regions with different scales. In our method,
we do not need to select a sized-fixed sub-window to differentiate similar
characters. The unconstrained property makes our method well adapted to high
variance in the size and position of discriminative regions in similar
handwritten Chinese characters. We evaluate our proposed approach over the
CASIA Chinese character data set and the results show that our method
outperforms the state of the art.
