  Handwriting of Chinese has long been an important skill in East Asia.
However, automatic generation of handwritten Chinese characters poses a great
challenge due to the large number of characters. Various machine learning
techniques have been used to recognize Chinese characters, but few works have
studied the handwritten Chinese character generation problem, especially with
unpaired training data. In this work, we formulate the Chinese handwritten
character generation as a problem that learns a mapping from an existing
printed font to a personalized handwritten style. We further propose DenseNet
CycleGAN to generate Chinese handwritten characters. Our method is applied not
only to commonly used Chinese characters but also to calligraphy work with
aesthetic values. Furthermore, we propose content accuracy and style
discrepancy as the evaluation metrics to assess the quality of the handwritten
characters generated. We then use our proposed metrics to evaluate the
generated characters from CASIA dataset as well as our newly introduced Lanting
calligraphy dataset.
