# Generative Adversarial Network for Handwritten Text

arXiv: https://arxiv.org/abs/1907.11845 · HTML: https://mlsanity.com/paper/1907.11845/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Handwritten Text Generation from Visual Archetypes](https://mlsanity.com/paper/2303.15269/) (2023)
- [VATr++: Choose Your Words Wisely for Handwritten Text Generation](https://mlsanity.com/paper/2402.10798/) (2024)
- [AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation](https://mlsanity.com/paper/2303.07012/) (2023)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning](https://mlsanity.com/paper/2409.09085/) (2024)
- [FSCNN: A Fast Sparse Convolution Neural Network Inference System](https://mlsanity.com/paper/2212.08815/) (2022)
- [Semi-Supervised Adaptation of Diffusion Models for Handwritten Text Generation](https://mlsanity.com/paper/2412.15853/) (2024)
- [S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing](https://mlsanity.com/paper/2404.08111/) (2024)
- [FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator](https://mlsanity.com/paper/2508.21040/) (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)
- [SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation](https://mlsanity.com/paper/2607.06949/) (2026)

## Abstract (arXiv)

Generative adversarial networks (GANs) have proven hugely successful in variety of applications of image processing. However, generative adversarial networks for handwriting is relatively rare somehow because of difficulty of handling sequential handwriting data by Convolutional Neural Network (CNN). In this paper, we propose a handwriting generative adversarial network framework (HWGANs) for synthesizing handwritten stroke data. The main features of the new framework include: (i) A discriminator consists of an integrated CNN-Long-Short-Term- Memory (LSTM) based feature extraction with Path Signature Features (PSF) as input and a Feedforward Neural Network (FNN) based binary classifier; (ii) A recurrent latent variable model as generator for synthesizing sequential handwritten data. The numerical experiments show the effectivity of the new model. Moreover, comparing with sole handwriting generator, the HWGANs synthesize more natural and realistic handwritten text.