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 this https URL
Abstract (translated)
标注的手写数据通常稀缺，这限制了需要多样化且风格一致的训练样本的识别系统的有效性。手写合成通过生成人工数据来扩充训练集，提供了一种有前景的解决方案。然而，当前的方法面临两个主要局限：首先，大多数方法建立在传统的卷积架构之上，这种架构难以建模长距离依赖关系和复杂的笔画模式；其次，它们很大程度上忽略了频域信息的关键作用，而这一信息对于捕捉手写中细微的风格和结构细节至关重要。 为了解决这些挑战，我们提出了FW-GAN（Frequency-aware Wave-MLP Generator），这是一种单样本的手写合成框架。该生成器集成了一个感知相位的Wave-MLP模块，以更好地捕获空间关系并保持微妙的风格线索。此外，我们引入了一个基于频域指导的判别器，利用高频成分来提高对生成样本真实性的检测能力。同时，还提出了一种新颖的频率分布损失函数，使合成的手写与真实的书写在频域特征上更加一致，从而提升视觉保真度。 实验表明，在越南语和英语手写数据集上的测试中，FW-GAN能够生成高质量且风格一致的手写样本，这使其成为低资源环境下扩充数据以提高手写识别（HTR）效果的重要工具。官方实现可在以下链接获取：[请在此处提供具体网址]
URL
https://arxiv.org/abs/2508.21040
