  We propose a novel transformer-based styled handwritten text image generation
approach, HWT, that strives to learn both style-content entanglement as well as
global and local writing style patterns. The proposed HWT captures the long and
short range relationships within the style examples through a self-attention
mechanism, thereby encoding both global and local style patterns. Further, the
proposed transformer-based HWT comprises an encoder-decoder attention that
enables style-content entanglement by gathering the style representation of
each query character. To the best of our knowledge, we are the first to
introduce a transformer-based generative network for styled handwritten text
generation. Our proposed HWT generates realistic styled handwritten text images
and significantly outperforms the state-of-the-art demonstrated through
extensive qualitative, quantitative and human-based evaluations. The proposed
HWT can handle arbitrary length of text and any desired writing style in a
few-shot setting. Further, our HWT generalizes well to the challenging scenario
where both words and writing style are unseen during training, generating
realistic styled handwritten text images.
