SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation
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. ...
| Authors | Nguyen Duy Hieu, Dang Hoai Nam, Pham Hoang Giap et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
Models and datasets referencing this paper
- DuyHieu63/SpiS_GANDataset
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