GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
This work addresses the challenge of producing plausible handwritten text for applications like document forgery detection or personalized fonts, representing a strong domain-specific advancement.
The paper tackles the problem of generating realistic and diverse handwritten word images by conditioning on both calligraphic style and textual content, achieving significant improvements over prior art as demonstrated through qualitative, quantitative, and human evaluations.
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.