# Adversarial Generation of Handwritten Text Images Conditioned on Sequences

arXiv: https://arxiv.org/abs/1903.00277 · HTML: https://mlsanity.com/paper/1903.00277/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 30 of 37)

- [Conditional Text Image Generation with Diffusion Models](https://mlsanity.com/paper/2306.10804/) (2023)
- [TextStyleBrush: Transfer of Text Aesthetics from a Single Example](https://mlsanity.com/paper/2106.08385/) (2021)
- [Handwriting Transformers](https://mlsanity.com/paper/2104.03964/) (2021)
- [Content and Style Aware Generation of Text-line Images for Handwriting Recognition](https://mlsanity.com/paper/2204.05539/) (2022)
- [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)
- [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)
- [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)
- [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://mlsanity.com/paper/2206.02234/) (2022)
- [How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning](https://mlsanity.com/paper/2305.02593/) (2023)
- [Zero-Shot Styled Text Image Generation, but Make It Autoregressive](https://mlsanity.com/paper/2503.17074/) (2025)
- [TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers](https://mlsanity.com/paper/2105.03906/) (2021)
- [Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation](https://mlsanity.com/paper/2508.03256/) (2025)
- [Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2305.19543/) (2023)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation](https://mlsanity.com/paper/2207.09649/) (2022)
- [Rethinking HTG Evaluation: Bridging Generation and Recognition](https://mlsanity.com/paper/2409.02683/) (2024)
- [Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2206.03149/) (2022)
- [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)
- [Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques](https://mlsanity.com/paper/2507.06275/) (2025)
- [Dual Orthogonal Guidance for Robust Diffusion-based Handwritten Text Generation](https://mlsanity.com/paper/2508.17017/) (2025)
- [TrInk: Ink Generation with Transformer Network](https://mlsanity.com/paper/2508.21098/) (2025)
- [A Study of Augmentation Methods for Handwritten Stenography Recognition](https://mlsanity.com/paper/2303.02761/) (2023)
- [MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks](https://mlsanity.com/paper/2312.16251/) (2023)
- [FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator](https://mlsanity.com/paper/2508.21040/) (2025)
- [Proceedings of the 4th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2211.13285/) (2022)

## Abstract (arXiv)

State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images. We obtain realistic images on both French and Arabic datasets, and we show that integrating these synthetic images into the existing training data of a text recognition system can slightly enhance its performance.