---

name: synthdog description: "Generate synthetic OCR-free document datasets with SynthDoG, render language-specific configs, and adapt the template for custom corpora." disable-model-invocation: true metadata: disco-role: operating license: MIT

SynthDoG

Use this sub-skill when a user wants to generate synthetic document samples, make OCR-free document data, swap between the English/Japanese/Korean/Chinese template configs, or adapt SynthDoG to a new corpus.

Route first

• Read references/workflows.md for the render-then-run flow, tiny-fixture smoke, split layout, and metadata layout.
• Read references/configuration.md for the config knobs, language bundle mapping, and custom-corpus adaptation.
• Read references/resource-layout.md for the background/paper/corpus/font directory contract and the asset bundling decision.
• Read references/troubleshooting.md when synthtiger, pytweening, NumPy, OpenCV, fonts, corpora, or output paths fail.
• Use scripts/render_config.py when you need to turn the bundled placeholder configs into a runnable config that points at external resource directories.
• For shared install/import issues with the broader Donut stack, check the parent-tree troubleshooting file at ../../references/troubleshooting.md once it exists.
• If the user is actually trying to fine-tune on generated data, hand off to ../training/SKILL.md.

Boundaries

• Include: template behavior, background/document/content/layout helper modules, language configs, resource requirements, CLI invocation through synthtiger, output directory and metadata layout, tiny-fixture generation.
• Exclude: Donut training, inference, and evaluation metrics.

Quick checks

• python scripts/render_config.py --help
• synthtiger --help if SynthDoG is installed in the active environment
• python -m py_compile scripts/render_config.py scripts/template.py scripts/elements/*.py scripts/layouts/*.py

What to do

1. Choose the language bundle and resource root.
2. Render a config with scripts/render_config.py.
3. Run synthtiger from this sub-skill root with scripts/template.py, the SynthDoG template class name, and the rendered config.
4. Inspect the split directories and metadata.jsonl files that template.py writes.
5. If the user later wants model training on the generated data, route the dataset to the training sub-skill.