  As of recent generative adversarial networks have allowed for big leaps in
the realism of generated images in diverse domains, not the least of which
being handwritten text generation. The generation of realistic-looking
hand-written text is important because it can be used for data augmentation in
handwritten text recognition (HTR) systems or human-computer interaction. We
propose SmartPatch, a new technique increasing the performance of current
state-of-the-art methods by augmenting the training feedback with a tailored
solution to mitigate pen-level artifacts. We combine the well-known patch loss
with information gathered from the parallel trained handwritten text
recognition system and the separate characters of the word. This leads to a
more enhanced local discriminator and results in more realistic and
higher-quality generated handwritten words.
