Document Type

Article

Publication Title

arXiv

Abstract

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. © 2021, CC BY.

DOI

doi.org/10.48550/arXiv.2104.03964

Publication Date

4-8-2021

Keywords

Computer Vision and Pattern Recognition (cs.CV)

Comments

Preprint: arXiv

  • Archived with thanks to arXiv
  • Preprint License: CC by 4.0
  • Uploaded 24 March 2022

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