Style Generation in Robot Calligraphy with Deep Generative Adversarial
  Networks
Xiaoming Wang Zhiguo Gong
Robot calligraphy is an emerging exploration of artificial intelligence in
the fields of art and education. Traditional calligraphy generation researches
mainly focus on methods such as tool-based image processing, generative models,
and style transfer. Unlike the English alphabet, the number of Chinese
characters is tens of thousands, which leads to difficulties in the generation
of a style consistent Chinese calligraphic font with over 6000 characters. Due
to the lack of high-quality data sets, formal definitions of calligraphy
knowledge, and scientific art evaluation methods, The results generated are
frequently of low quality and falls short of professional-level requirements.
To address the above problem, this paper proposes an automatic calligraphy
generation model based on deep generative adversarial networks (deepGAN) that
can generate style calligraphy fonts with professional standards. The key
highlights of the proposed method include: (1) The datasets use a
high-precision calligraphy synthesis method to ensure its high quality and
sufficient quantity; (2) Professional calligraphers are invited to conduct a
series of Turing tests to evaluate the gap between model generation results and
human artistic level; (3) Experimental results indicate that the proposed model
is the state-of-the-art among current calligraphy generation methods. The
Turing tests and similarity evaluations validate the effectiveness of the
proposed method.