Text and Style Conditioned GAN for Generation of Offline Handwriting Lines
Brian P. Davis, Chris Tensmeyer, Brian L. Price, Curtis Wigington, Bryan S. Morse, Rajiv Ratan Jain · arXiv (Cornell University) · 2020
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.