# Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation

arXiv: https://arxiv.org/abs/2508.03256 · HTML: https://mlsanity.com/paper/2508.03256/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 69 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [No Fuss Distance Metric Learning using Proxies](https://mlsanity.com/paper/1703.07464/) (2017)
- [Diffusion-GAN: Training GANs with Diffusion](https://mlsanity.com/paper/2206.02262/) (2022)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs](https://mlsanity.com/paper/2311.09257/) (2023)
- [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)
- [Expanding Small-Scale Datasets with Guided Imagination](https://mlsanity.com/paper/2211.13976/) (2022)
- [Conditional Text Image Generation with Diffusion Models](https://mlsanity.com/paper/2306.10804/) (2023)
- [Geometry Score: A Method For Comparing Generative Adversarial Networks](https://mlsanity.com/paper/1802.02664/) (2018)
- [MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation](https://mlsanity.com/paper/2412.04448/) (2024)
- [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)
- [Disentangling Writer and Character Styles for Handwriting Generation](https://mlsanity.com/paper/2303.14736/) (2023)
- [Handwritten Text Generation from Visual Archetypes](https://mlsanity.com/paper/2303.15269/) (2023)
- [SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text](https://mlsanity.com/paper/2202.11456/) (2022)
- [WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models](https://mlsanity.com/paper/2303.16576/) (2023)
- [DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding](https://mlsanity.com/paper/2408.15045/) (2024)
- [Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning](https://mlsanity.com/paper/2409.01128/) (2024)

## Built on by (most cited first, 10 of 10)

- [HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding](https://mlsanity.com/paper/2602.12957/) (2026)
- [Order-Level Attention Similarity Across Language Models: A Latent Commonality](https://mlsanity.com/paper/2511.05064/) (2025)
- [SkyReels-Text: Fine-Grained Font-Controllable Text Editing for Poster Design](https://mlsanity.com/paper/2511.13285/) (2025)
- [OrbitNVS: Harnessing Video Diffusion Priors for Novel View Synthesis](https://mlsanity.com/paper/2603.19613/) (2026)
- [Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD](https://mlsanity.com/paper/2604.10075/) (2026)
- [Diffusion-Based Ukrainian Handwritten Text Generation with Cross-Domain Style Transfer](https://mlsanity.com/paper/2605.27487/) (2026)
- [DIMOS: Disentangling Instance-level Moving Object Segmentation](https://mlsanity.com/paper/2606.12826/) (2026)
- [DiffMath: Symbol- and Graph-Aware Latent Diffusion Transformer for Handwritten Mathematical Expression Generation](https://mlsanity.com/paper/2606.19939/) (2026)
- [InkShield: Writing Style Protection Against Unauthorized Handwriting Mimicry](https://mlsanity.com/paper/2607.26976/) (2026)
- [UniLipi: A Unified Multi-Script OCR for Historical Indic Manuscripts](https://mlsanity.com/paper/2608.28195/) (2026)

## Abstract (arXiv)

Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text lines emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns encompassing both intra- and inter-word relationships, and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush.