Handwritten Text Generation from Visual Archetypes
Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara

TL;DR
This paper introduces a Transformer-based model for few-shot handwritten text generation that effectively captures writer style and rare characters by using visual archetypes and pre-training on synthetic data, outperforming existing methods.
Contribution
The work presents a novel representation of textual content as visual archetypes and leverages pre-training to improve style and character generalization in handwritten text synthesis.
Findings
Effective generation of unseen styles and rare characters.
Outperforms existing approaches in fidelity of generated handwriting.
Robust representation of writer style through synthetic pre-training.
Abstract
Generating synthetic images of handwritten text in a writer-specific style is a challenging task, especially in the case of unseen styles and new words, and even more when these latter contain characters that are rarely encountered during training. While emulating a writer's style has been recently addressed by generative models, the generalization towards rare characters has been disregarded. In this work, we devise a Transformer-based model for Few-Shot styled handwritten text generation and focus on obtaining a robust and informative representation of both the text and the style. In particular, we propose a novel representation of the textual content as a sequence of dense vectors obtained from images of symbols written as standard GNU Unifont glyphs, which can be considered their visual archetypes. This strategy is more suitable for generating characters that, despite having been…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Human Motion and Animation · Generative Adversarial Networks and Image Synthesis
