Total Disentanglement of Font Images into Style and Character Class
  Features
Daichi Haraguchi Wataru Shimoda Kota Yamaguchi Seiichi Uchida
In this paper, we demonstrate a total disentanglement of font images. Total
disentanglement is a neural network-based method for decomposing each font
image nonlinearly and completely into its style and content (i.e., character
class) features. It uses a simple but careful training procedure to extract the
common style feature from all `A'-`Z' images in the same font and the common
content feature from all `A' (or another class) images in different fonts.
These disentangled features guarantee the reconstruction of the original font
image. Various experiments have been conducted to understand the performance of
total disentanglement. First, it is demonstrated that total disentanglement is
achievable with very high accuracy; this is experimental proof of the
long-standing open question, ``Does `A'-ness exist?'' Hofstadter (1985).
Second, it is demonstrated that the disentangled features produced by total
disentanglement apply to a variety of tasks, including font recognition,
character recognition, and one-shot font image generation.