  Although current image generation methods have reached impressive quality
levels, they are still unable to produce plausible yet diverse images of
handwritten words. On the contrary, when writing by hand, a great variability
is observed across different writers, and even when analyzing words scribbled
by the same individual, involuntary variations are conspicuous. In this work,
we take a step closer to producing realistic and varied artificially rendered
handwritten words. We propose a novel method that is able to produce credible
handwritten word images by conditioning the generative process with both
calligraphic style features and textual content. Our generator is guided by
three complementary learning objectives: to produce realistic images, to
imitate a certain handwriting style and to convey a specific textual content.
Our model is unconstrained to any predefined vocabulary, being able to render
whatever input word. Given a sample writer, it is also able to mimic its
calligraphic features in a few-shot setup. We significantly advance over prior
art and demonstrate with qualitative, quantitative and human-based evaluations
the realistic aspect of our synthetically produced images.
