  We describe a novel method of generating high-resolution real-world images of
text where the style and textual content of the images are described
parametrically. Our method combines text to image retrieval techniques with
progressive growing of Generative Adversarial Networks (PGGANs) to achieve
conditional generation of photo-realistic images that reflect specific styles,
as well as artifacts seen in real-world images. We demonstrate our method in
the context of automotive license plates. We assess the impact of varying the
number of training images of each style on the fidelity of the generated style,
and demonstrate the quality of the generated images using license plate
recognition systems.
