  In this paper, we present an effective method to analyze the recognition
confidence of handwritten Chinese character, based on the softmax regression
score of a high performance convolutional neural networks (CNN). Through
careful and thorough statistics of 827,685 testing samples that randomly
selected from total 8836 different classes of Chinese characters, we find that
the confidence measurement based on CNN is an useful metric to know how
reliable the recognition results are. Furthermore, we find by experiments that
the recognition confidence can be used to find out similar and confusable
character-pairs, to check wrongly or cursively written samples, and even to
discover and correct mis-labelled samples. Many interesting observations and
statistics are given and analyzed in this study.

Computer Vision and Pattern Recognition