# Handwritten Text Generation from Visual Archetypes

arXiv: https://arxiv.org/abs/2303.15269 · HTML: https://mlsanity.com/paper/2303.15269/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 55 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Geometric GAN](https://mlsanity.com/paper/1705.02894/) (2017)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation](https://mlsanity.com/paper/2003.10557/) (2020)
- [GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images](https://mlsanity.com/paper/2003.02567/) (2020)
- [Geometry Score: A Method For Comparing Generative Adversarial Networks](https://mlsanity.com/paper/1802.02664/) (2018)
- [Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning](https://mlsanity.com/paper/1910.04987/) (2019)
- [TextStyleBrush: Transfer of Text Aesthetics from a Single Example](https://mlsanity.com/paper/2106.08385/) (2021)
- [Few-shot Font Generation with Localized Style Representations and Factorization](https://mlsanity.com/paper/2009.11042/) (2020)
- [Few-shot Compositional Font Generation with Dual Memory](https://mlsanity.com/paper/2005.10510/) (2020)
- [DeepWriting: Making Digital Ink Editable via Deep Generative Modeling](https://mlsanity.com/paper/1801.08379/) (2018)
- [Adversarial Generation of Handwritten Text Images Conditioned on Sequences](https://mlsanity.com/paper/1903.00277/) (2019)
- [Handwriting Transformers](https://mlsanity.com/paper/2104.03964/) (2021)
- [Text and Style Conditioned GAN for Generation of Offline Handwriting Lines](https://mlsanity.com/paper/2009.00678/) (2020)
- [Content and Style Aware Generation of Text-line Images for Handwriting Recognition](https://mlsanity.com/paper/2204.05539/) (2022)
- [Handwriting Recognition in Low-resource Scripts using Adversarial Learning](https://mlsanity.com/paper/1811.01396/) (2018)
- [MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition](https://mlsanity.com/paper/2104.01876/) (2021)
- [STCN: Stochastic Temporal Convolutional Networks](https://mlsanity.com/paper/1902.06568/) (2019)
- [SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text](https://mlsanity.com/paper/2202.11456/) (2022)
- [Generating Handwriting via Decoupled Style Descriptors](https://mlsanity.com/paper/2008.11354/) (2020)
- [Generating Handwritten Chinese Characters using CycleGAN](https://mlsanity.com/paper/1801.08624/) (2018)
- [SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators](https://mlsanity.com/paper/2105.10528/) (2021)
- [Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation](https://mlsanity.com/paper/2107.12087/) (2021)
- [Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions](https://mlsanity.com/paper/2208.08109/) (2022)

## Built on by (most cited first, 30 of 32)

- [One-Shot Diffusion Mimicker for Handwritten Text Generation](https://mlsanity.com/paper/2409.04004/) (2024)
- [HWD: A Novel Evaluation Score for Styled Handwritten Text Generation](https://mlsanity.com/paper/2310.20316/) (2023)
- [VATr++: Choose Your Words Wisely for Handwritten Text Generation](https://mlsanity.com/paper/2402.10798/) (2024)
- [DiffusionPen: Towards Controlling the Style of Handwritten Text Generation](https://mlsanity.com/paper/2409.06065/) (2024)
- [Zero-Shot Styled Text Image Generation, but Make It Autoregressive](https://mlsanity.com/paper/2503.17074/) (2025)
- [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025)
- [On the Generalization of Handwritten Text Recognition Models](https://mlsanity.com/paper/2411.17332/) (2024)
- [Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation](https://mlsanity.com/paper/2508.03256/) (2025)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [Rethinking HTG Evaluation: Bridging Generation and Recognition](https://mlsanity.com/paper/2409.02683/) (2024)
- [Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models](https://mlsanity.com/paper/2409.00786/) (2024)
- [Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence](https://mlsanity.com/paper/2601.12318/) (2026)
- [Autoregressive Styled Text Image Generation, but Make it Reliable](https://mlsanity.com/paper/2510.23240/) (2025)
- [StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation](https://mlsanity.com/paper/2407.15608/) (2024)
- [Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques](https://mlsanity.com/paper/2507.06275/) (2025)
- [Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?](https://mlsanity.com/paper/2508.09936/) (2025)
- [Dual Orthogonal Guidance for Robust Diffusion-based Handwritten Text Generation](https://mlsanity.com/paper/2508.17017/) (2025)
- [FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator](https://mlsanity.com/paper/2508.21040/) (2025)
- [DiffInk: Glyph- and Style-Aware Latent Diffusion Transformer for Text to Online Handwriting Generation](https://mlsanity.com/paper/2509.23624/) (2025)
- [SkyReels-Text: Fine-Grained Font-Controllable Text Editing for Poster Design](https://mlsanity.com/paper/2511.13285/) (2025)
- [CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization](https://mlsanity.com/paper/2603.07543/) (2026)
- [Align, Minimize and Diversify: A Source-Free Unsupervised Domain Adaptation Method for Handwritten Text Recognition](https://mlsanity.com/paper/2404.18260/) (2024)
- [Machine Apophenia: The Kaleidoscopic Generation of Architectural Images](https://mlsanity.com/paper/2407.09172/) (2024)
- [WriteViT: Handwritten Text Generation with Vision Transformer](https://mlsanity.com/paper/2505.13235/) (2025)
- [Layout Stroke Imitation: A Layout Guided Handwriting Stroke Generation for Style Imitation with Diffusion Model](https://mlsanity.com/paper/2509.15678/) (2025)
- [ScriptViT: Vision Transformer-Based Personalized Handwriting Generation](https://mlsanity.com/paper/2511.18307/) (2025)
- [BeHGAN: Bengali Handwritten Word Generation from Plain Text Using Generative Adversarial Networks](https://mlsanity.com/paper/2512.21694/) (2025)
- [Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies](https://mlsanity.com/paper/2604.09713/) (2026)
- [Diffusion-Based Ukrainian Handwritten Text Generation with Cross-Domain Style Transfer](https://mlsanity.com/paper/2605.27487/) (2026)
- [DiffMath: Symbol- and Graph-Aware Latent Diffusion Transformer for Handwritten Mathematical Expression Generation](https://mlsanity.com/paper/2606.19939/) (2026)

## Abstract (arXiv)

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 seen rarely during training, possibly share visual details with the frequently observed ones. As for the style, we obtain a robust representation of unseen writers' calligraphy by exploiting specific pre-training on a large synthetic dataset. Quantitative and qualitative results demonstrate the effectiveness of our proposal in generating words in unseen styles and with rare characters more faithfully than existing approaches relying on independent one-hot encodings of the characters.