SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation

📄 arXiv: 2607.06949v1 📥 PDF

作者: Nguyen Duy Hieu, Dang Hoai Nam, Pham Hoang Giap, Quang Huu Hieu, Vo Nguyen Le Duy

分类: cs.CV

发布日期: 2026-07-08

💡 一句话要点

提出SpiS-GAN以解决手写体合成中的结构细节缺失问题

🎯 匹配领域: 支柱七：动作重定向 (Motion Retargeting)

关键词: 手写体合成 生成对抗网络 深度学习 图像生成 机器学习

📋 核心要点

  1. 现有手写体合成模型在追踪连笔手写时存在固定网格感受野的局限，导致结构细节丢失。
  2. 本文提出SpiS-GAN框架，通过星形螺旋块和调制椭圆螺旋全连接层有效捕捉复杂手写轨迹。
  3. 实验结果显示，SpiS-GAN在生成图像的真实性和风格保留上显著优于现有模型，降低了下游手写识别系统的错误率。

📝 摘要（中文）

训练稳健的手写识别系统需要大量标注数据，而合成手写生成提供了扩展训练集的有效解决方案。然而，现有模型在追踪连笔手写时存在固定网格空间感受野的局限，且CNN判别器在下采样过程中容易丢失结构细节，导致连接断裂难以检测。此外，现有架构对特征交互的限制或高分辨率合成的高成本也影响了效果。为此，本文提出了一种基于生成对抗网络的螺旋调制手写合成框架SpiS-GAN，采用星形螺旋块和调制椭圆螺旋全连接层来捕捉空间关系，并引入螺旋调制判别器进行多域缺陷检测，同时采用Sobel正则化边缘重建损失确保字符清晰可读。实验结果表明，SpiS-GAN在英语和越南语数据集上显著优于现有最先进模型。

🔬 方法详解

问题定义：本文旨在解决现有手写体合成模型在追踪连笔手写时的结构细节缺失和模糊边界问题。现有方法由于固定网格感受野和下采样过程中的信息丢失，难以生成清晰的手写字符。

核心思路：论文提出的SpiS-GAN框架通过引入星形螺旋块和调制椭圆螺旋全连接层，能够有效捕捉手写轨迹的空间关系，从而生成更为真实的手写体图像。

技术框架：SpiS-GAN的整体架构包括生成器和判别器两个主要模块。生成器采用星形螺旋块来生成手写图像，而判别器则通过螺旋调制技术进行多域缺陷检测。

关键创新：最重要的创新在于引入了Sobel正则化边缘重建损失，提供了边缘引导，确保生成字符的清晰度和可读性。这一设计显著改善了手写体合成的质量。

关键设计：生成器中的星形螺旋块和调制椭圆螺旋全连接层是关键设计，能够有效捕捉复杂的手写轨迹。同时，采用的损失函数包括Sobel正则化损失，确保生成的字符边缘清晰可辨。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，SpiS-GAN在英语和越南语数据集上生成的手写图像在真实性和风格保留方面显著优于现有最先进模型，降低了下游手写识别系统的错误率，具体提升幅度未知。

🎯 应用场景

该研究的潜在应用领域包括手写识别系统的训练和增强，尤其是在数据稀缺的情况下。通过生成高质量的合成手写数据，能够有效提升手写识别模型的性能，具有重要的实际价值和广泛的应用前景。

📄 摘要（原文）

Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data, which is often difficult to acquire. While synthetic handwriting generation offers a practical solution to expand training sets, existing models struggle with several core issues. First, previous approaches, even MLP-based models fail to effectively trace cursive handwriting due to fixed-grid spatial receptive field. Second, their CNN-relied discriminators usually lose structural details through aggressive downsampling, making broken connections difficult to detect. Third, existing architectures are either limited to linear feature interactions or too expensive for high-resolution synthesis. Finally, existing approaches lack explicit edge constraints, often resulting in blurred stroke boundaries. To address these challenges, this study proposes a Spiral-Modulated Handwriting Synthesis framework based on Generative Adversarial Networks (SpiS-GAN). Our generator employs Star-Spiral Blocks combining proposed Modulated Elliptical SpiralFC with the star operation to capture spatial relationships and efficiently follow complex handwriting stroke trajectories, while a Spiral-Modulated discriminator is introduced for multi-domain flaws detection. Additionally, we introduce a Sobel-Regularized Edge Reconstruction Loss that provides edge guidance, ensuring every character remains clear and legible. Evaluations on the English and Vietnamese datasets demonstrate that SpiS-GAN significantly outperforms current state-of-the-art models. The generated images are highly authentic, accurately preserve the original writer's style across languages, and successfully lower error rates when training downstream HTR systems.