# DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

arXiv: https://arxiv.org/abs/1801.08379 · HTML: https://mlsanity.com/paper/1801.08379/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 30 of 39)

- [Handwriting Transformers](https://mlsanity.com/paper/2104.03964/) (2021)
- [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)
- [MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition](https://mlsanity.com/paper/2104.01876/) (2021)
- [Diffusion models for Handwriting Generation](https://mlsanity.com/paper/2011.06704/) (2020)
- [One-Shot Diffusion Mimicker for Handwritten Text Generation](https://mlsanity.com/paper/2409.04004/) (2024)
- [VATr++: Choose Your Words Wisely for Handwritten Text Generation](https://mlsanity.com/paper/2402.10798/) (2024)
- [SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators](https://mlsanity.com/paper/2105.10528/) (2021)
- [SketchINR: A First Look into Sketches as Implicit Neural Representations](https://mlsanity.com/paper/2403.09344/) (2024)
- [Zero-Shot Styled Text Image Generation, but Make It Autoregressive](https://mlsanity.com/paper/2503.17074/) (2025)
- [Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models](https://mlsanity.com/paper/2110.02891/) (2021)
- [AniFaceDrawing: Anime Portrait Exploration during Your Sketching](https://mlsanity.com/paper/2306.07476/) (2023)
- [Representing Online Handwriting for Recognition in Large Vision-Language Models](https://mlsanity.com/paper/2402.15307/) (2024)
- [Decoupling Layout from Glyph in Online Chinese Handwriting Generation](https://mlsanity.com/paper/2410.02309/) (2024)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models](https://mlsanity.com/paper/2409.00786/) (2024)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence](https://mlsanity.com/paper/2601.12318/) (2026)
- [Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation](https://mlsanity.com/paper/2309.03072/) (2023)
- [Autoregressive Styled Text Image Generation, but Make it Reliable](https://mlsanity.com/paper/2510.23240/) (2025)
- [DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation](https://mlsanity.com/paper/2311.17786/) (2023)
- [InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write](https://mlsanity.com/paper/2402.05804/) (2024)
- [InkFM: A Foundational Model for Full-Page Online Handwritten Note Understanding](https://mlsanity.com/paper/2503.23081/) (2025)
- [Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques](https://mlsanity.com/paper/2507.06275/) (2025)
- [TrInk: Ink Generation with Transformer Network](https://mlsanity.com/paper/2508.21098/) (2025)
- [Data Incubation -- Synthesizing Missing Data for Handwriting Recognition](https://mlsanity.com/paper/2110.07040/) (2021)
- [Inkorrect: Online Handwriting Spelling Correction](https://mlsanity.com/paper/2202.13794/) (2022)
- [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)
- [LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space](https://mlsanity.com/paper/2203.07881/) (2022)

## Abstract (arXiv)

Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text. Character recognition converts handwritten text into a digital representation, albeit at the cost of losing personalized appearance due to the technical difficulties of separating the interwoven components of content and style. In this paper, we propose a novel generative neural network architecture that is capable of disentangling style from content and thus making digital ink editable. Our model can synthesize arbitrary text, while giving users control over the visual appearance (style). For example, allowing for style transfer without changing the content, editing of digital ink at the word level and other application scenarios such as spell-checking and correction of handwritten text. We furthermore contribute a new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.