Implementation of GANWriting, Content-Conditioned Generation of Styled Handwritten Word Images
Published:
Internship, 2021
Worked on the deployment of a state-of-the-art GAN model for generating hand written words, with the goal of writing a full library in Pytorch Lightning. This framework relied on a deep understanding of Computer Vision and complex ML system. GANWriting 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. The 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.