  This paper presents a GAN for generating images of handwritten lines
conditioned on arbitrary text and latent style vectors. Unlike prior work,
which produce stroke points or single-word images, this model generates entire
lines of offline handwriting. The model produces variable-sized images by using
style vectors to determine character widths. A generator network is trained
with GAN and autoencoder techniques to learn style, and uses a pre-trained
handwriting recognition network to induce legibility. A study using human
evaluators demonstrates that the model produces images that appear to be
written by a human. After training, the encoder network can extract a style
vector from an image, allowing images in a similar style to be generated, but
with arbitrary text.
