Similar handwritten Chinese characters recognition by critical region selection based on average symmetric uncertainty

We consider the problem of similar Chinese character recognition in this paper. Engaging the Average Symmetric Uncertainty (ASU) criterion to measure the correlation between different image regions and the class label, we manage to detect the most critical regions for each pair of similar characters. These critical regions are proved to contain more discriminative information and hence can largely benefit the classification accuracy for similar characters. We conduct a series of experiments on the CASIA Chinese character data set. Experimental results show that our proposed method is superior to three competitive approaches in terms of both accuracy and efficiency. © 2010 IEEE.

Duke Scholars

Author Kaizhu Huang DKU Faculty