  Optical character recognition (OCR) systems performance have improved
significantly in the deep learning era. This is especially true for handwritten
text recognition (HTR), where each author has a unique style, unlike printed
text, where the variation is smaller by design. That said, deep learning based
HTR is limited, as in every other task, by the number of training examples.
Gathering data is a challenging and costly task, and even more so, the labeling
task that follows, of which we focus here. One possible approach to reduce the
burden of data annotation is semi-supervised learning. Semi supervised methods
use, in addition to labeled data, some unlabeled samples to improve
performance, compared to fully supervised ones. Consequently, such methods may
adapt to unseen images during test time.
  We present ScrabbleGAN, a semi-supervised approach to synthesize handwritten
text images that are versatile both in style and lexicon. ScrabbleGAN relies on
a novel generative model which can generate images of words with an arbitrary
length. We show how to operate our approach in a semi-supervised manner,
enjoying the aforementioned benefits such as performance boost over state of
the art supervised HTR. Furthermore, our generator can manipulate the resulting
text style. This allows us to change, for instance, whether the text is
cursive, or how thin is the pen stroke.
