Handwriting Transformers

• URL: http://arxiv.org/abs/2104.03964v1
• Date: Thu, 8 Apr 2021 17:59:43 GMT
• Title: Handwriting Transformers
• Authors: Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Mubarak Shah
• Abstract summary: We propose a transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement and 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. Our proposed HWT generates realistic styled handwritten text images and significantly outperforms the state-of-the-art demonstrated.
• Score: 98.3964093654716
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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