Research map / Image generation / Autoregressive and latent generation
Handwritten Text Generation From Visual Archetypes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add VATr-generated images to real-data HTR training | HTR model · Saint Gall | WER: 32.5 -> 30.9 | Helped |
| Add VATr-generated images to real-data HTR training | HTR model · Saint Gall | CER: 4.5 -> 4.5 | No clear effect |
| Add VATr-generated images to real-data HTR training | HTR model · Washington | CER: 3.4 -> 3.0 | Helped |
| Add VATr-generated images to real-data HTR training | HTR model · Washington | WER: 15.9 -> 13.1 | Helped |
| Add L_HTR text-recognition loss (ablation) | VATr · IAM word 'that', two styles | FID: 220.83 -> 17.79 | Helped |
| Add L_class writer-classification loss (ablation) | VATr · IAM word 'that', two styles | FID: 41.68 -> 17.79 | Helped |
| Add L_cycle cycle loss (ablation) | VATr · IAM word 'that', two styles | FID: 18.70 -> 17.79 | Helped |
Closest papers by meaning
- Zero-Shot Styled Text Image Generation, but Make It Autoregressive (CVPR 2025 · similarity 0.89)
- DreamText: High Fidelity Scene Text Synthesis (CVPR 2025 · similarity 0.87)
- Decoupling Layout from Glyph in Online Chinese Handwriting Generation (ICLR 2025 · similarity 0.86)
- VecGlypher: Unified Vector Glyph Generation with Language Models (CVPR 2026 · similarity 0.85)
- Layout-Agnostic Scene Text Image Synthesis with Diffusion Models (CVPR 2024 · similarity 0.84)
- Few-Shot Detection of Machine-Generated Text using Style Representations (ICLR 2024 · similarity 0.84)
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