  In this paper, we investigate the Chinese font synthesis problem and propose
a Pyramid Embedded Generative Adversarial Network (PEGAN) to automatically
generate Chinese character images. The PEGAN consists of one generator and one
discriminator. The generator is built using one encoder-decoder structure with
cascaded refinement connections and mirror skip connections. The cascaded
refinement connections embed a multiscale pyramid of downsampled original input
into the encoder feature maps of different layers, and multi-scale feature maps
from the encoder are connected to the corresponding feature maps in the decoder
to make the mirror skip connections. Through combining the generative
adversarial loss, pixel-wise loss, category loss and perceptual loss, the
generator and discriminator can be trained alternately to synthesize character
images. In order to verify the effectiveness of our proposed PEGAN, we first
build one evaluation set, in which the characters are selected according to
their stroke number and frequency of use, and then use both qualitative and
quantitative metrics to measure the performance of our model comparing with the
baseline method. The experimental results demonstrate the effectiveness of our
proposed model, it shows the potential to automatically extend small font banks
into complete ones.
