  The authenticity of calligraphy is significant but difficult task in the
realm of art, where the key problem is the few-shot classification of
calligraphy. We propose a novel method, ShufaNet ("Shufa" is the pinyin of
Chinese calligraphy), to classify Chinese calligraphers' styles based on metric
learning in the case of few-shot, whose classification accuracy exceeds the
level of students majoring in calligraphy. We present a new network
architecture, including the unique expression of the style of handwriting fonts
called ShufaLoss and the calligraphy category information as prior knowledge.
Meanwhile, we modify the spatial attention module and create ShufaAttention for
handwriting fonts based on the traditional Chinese nine Palace thought. For the
training of the model, we build a calligraphers' data set. Our method achieved
65% accuracy rate in our data set for few-shot learning, surpassing resNet and
other mainstream CNNs. Meanwhile, we conducted battle for calligraphy major
students, and finally surpassed them. This is the first attempt of deep
learning in the field of calligrapher classification, and we expect to provide
ideas for subsequent research.
