- name
- ocr2markdown
- description
- Document OCR and parsing — converts PDF/images to Markdown on remote L4 GPU via Modal. Trigger when user says: OCR, PDF to markdown, parse PDF, extract text from PDF, 文档识别, PDF转Markdown, 扫描件识别. Takes local PDF/image files and returns Markdown with layout, tables, formulas, and OCR preserved.
- version
- v1.0.1
OCR2Markdown
MinerU document parsing pipeline on Modal L4 GPU — PDF/image → Markdown with layout, tables, formulas, and OCR preserved.
Pipeline code is bundled at ./src/ocr2markdown.py. After npx skills add, runs from any directory.
Workflow
1. Prepare slug and identify files
Slug = task identifier (volume directory name). Use user-provided value, or generate ocr_YYYYMMDD_HHMMSS if none given.
Directory input? Scan for PDF (.pdf), list with index, ask user to confirm selection.
Specific files? Use directly, no listing needed.
2. Upload to volume
Ensure both volumes exist (idempotent):
modal volume create speech2srt-data 2>/dev/null || true
modal volume create speech2srt-models 2>/dev/null || trueUpload each file:
modal volume put speech2srt-data <local_file> <slug>/upload/Modal put auto-creates remote directories.
3. Run pipeline
modal run ./src/ocr2markdown.py --slug <slug>Pipeline finds all .pdf files in <slug>/upload/ on the volume and processes them one by one.
Ctrl+C? Stop cleanly, report progress. Re-run with same slug — already-processed PDFs are skipped automatically.
4. Download results
Output directory structure:
<slug>/output/<stem>_ocr/
├── <stem>.md # Main markdown output
├── images/ # Extracted images referenced by markdown
└── *.pdf, *.json # Auxiliary outputs (layout, model data)Download the entire output folder:
modal volume get speech2srt-data <slug>/output/ <local_destination>/Preserve original directory tree.
5. Report
Done. Parsed N PDF(s) in Xs
Results:
- <output_dir>/<stem>_ocr/<stem>.md
- <output_dir>/<stem>_ocr/images/Setup
Before first run, verify:
- Python 3.9+ —
python -V - Modal CLI —
modal config show:
- token_id null → modal setup to authenticate - command not found → pip install modal then modal setup
- Modal free tier — L4 GPU is $0.80/hr. New accounts get $30 free credits/month (~37 hours of L4). No setup needed beyond authentication.
Performance
| PDF Size | Pages | Time/PDF |
|---|---|---|
| ~40-55 MB | varies | ~110-130s each |
Pipeline auto-skips PDFs with existing output.