  State-of-the-art offline handwriting text recognition systems tend to use
neural networks and therefore require a large amount of annotated data to be
trained. In order to partially satisfy this requirement, we propose a system
based on Generative Adversarial Networks (GAN) to produce synthetic images of
handwritten words. We use bidirectional LSTM recurrent layers to get an
embedding of the word to be rendered, and we feed it to the generator network.
We also modify the standard GAN by adding an auxiliary network for text
recognition. The system is then trained with a balanced combination of an
adversarial loss and a CTC loss. Together, these extensions to GAN enable to
control the textual content of the generated word images. We obtain realistic
images on both French and Arabic datasets, and we show that integrating these
synthetic images into the existing training data of a text recognition system
can slightly enhance its performance.
