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.

我们提出了一种新型的基于Transformer的手写文本样式图像生成方法，旨在学习样式-内容的缠结以及全局和局部写作风格模式。通过自注意机制，所提出的HWT捕捉了样式示例中的长距离和短距离关系，从而编码了全局和局部样式模式。此外，所提出的基于Transformer的HWT包括编码器-解码器注意力，通过聚集每个查询字符的样式表示来实现样式-内容缠结。

手写变形器