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

• URL: http://arxiv.org/abs/2607.06949v1
• Date: Wed, 08 Jul 2026 03:21:19 GMT
• Title: SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation
• Abstract summary: Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data.<n>Previous approaches fail to effectively trace cursive handwriting due to fixed-grid receptive field.<n>Existing approaches lack explicit edge constraints, often resulting in blurred stroke boundaries.<n>This study proposes a Spiral-Modulated Handwriting Synthesis framework based on Generative Adrial Networks (SpiS-GAN)
• Score: 4.223500604789643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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