Abstract
In this work, we propose an entirely learning-based
method to automatically synthesize text sequence
in natural images leveraging conditional adversarial networks. As vanilla GANs are clumsy to
capture structural text patterns, directly employing GANs for text image synthesis typically results in illegible images. Therefore, we design a
two-stage architecture to generate repeated characters in images. Firstly, a character generator attempts to synthesize local character appearance independently, so that the legible characters in sequence can be obtained. To achieve style consistency of characters, we propose a novel style
loss based on variance-minimization. Secondly, we
design a pixel-manipulation word generator constrained by self-regularization, which learns to convert local characters to plausible word image. Experiments on SVHN dataset and ICDAR, IIIT5K
datasets demonstrate our method is able to synthesize visually appealing text images. Besides, we
also show the high-quality images synthesized by
our method can be used to boost the performance
of a scene text recognition algorithm