# FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator

## TL;DR

FW-GAN is a novel one-shot handwriting synthesis framework that leverages frequency information and a wave-modulated MLP generator to produce realistic, style-consistent handwriting from a single example, addressing limitations of previous methods.

## Contribution

The paper introduces FW-GAN, which integrates a phase-aware Wave-MLP and frequency-guided discriminator to improve handwriting synthesis quality and style consistency from minimal data.

## Key findings

- Generates high-quality, style-consistent handwriting for Vietnamese and English datasets.
- Outperforms existing methods in realism and stylistic accuracy.
- Enhances data augmentation for low-resource handwriting recognition systems.

## Abstract

Labeled handwriting data is often scarce, limiting the effectiveness of recognition systems that require diverse, style-consistent training samples. Handwriting synthesis offers a promising solution by generating artificial data to augment training. However, current methods face two major limitations. First, most are built on conventional convolutional architectures, which struggle to model long-range dependencies and complex stroke patterns. Second, they largely ignore the crucial role of frequency information, which is essential for capturing fine-grained stylistic and structural details in handwriting. To address these challenges, we propose FW-GAN, a one-shot handwriting synthesis framework that generates realistic, writer-consistent text from a single example. Our generator integrates a phase-aware Wave-MLP to better capture spatial relationships while preserving subtle stylistic cues. We further introduce a frequency-guided discriminator that leverages high-frequency components to enhance the authenticity detection of generated samples. Additionally, we introduce a novel Frequency Distribution Loss that aligns the frequency characteristics of synthetic and real handwriting, thereby enhancing visual fidelity. Experiments on Vietnamese and English handwriting datasets demonstrate that FW-GAN generates high-quality, style-consistent handwriting, making it a valuable tool for augmenting data in low-resource handwriting recognition (HTR) pipelines. Official implementation is available at https://github.com/DAIR-Group/FW-GAN