  We present a novel approach for disentangling the content of a text image
from all aspects of its appearance. The appearance representation we derive can
then be applied to new content, for one-shot transfer of the source style to
new content. We learn this disentanglement in a self-supervised manner. Our
method processes entire word boxes, without requiring segmentation of text from
background, per-character processing, or making assumptions on string lengths.
We show results in different text domains which were previously handled by
specialized methods, e.g., scene text, handwritten text. To these ends, we make
a number of technical contributions: (1) We disentangle the style and content
of a textual image into a non-parametric, fixed-dimensional vector. (2) We
propose a novel approach inspired by StyleGAN but conditioned over the example
style at different resolution and content. (3) We present novel self-supervised
training criteria which preserve both source style and target content using a
pre-trained font classifier and text recognizer. Finally, (4) we also introduce
Imgur5K, a new challenging dataset for handwritten word images. We offer
numerous qualitative photo-realistic results of our method. We further show
that our method surpasses previous work in quantitative tests on scene text and
handwriting datasets, as well as in a user study.
