| name | pdf-ocr |
| description | Process scanned or image-based PDFs by rendering pages to images for vision analysis or extracting embedded text. Use when filesystem.read_document returns no text from a PDF. |
PDF OCR Skill
Use this skill when filesystem.read_document fails to extract text from a PDF — typically because the PDF contains scanned images rather than embedded text. This is common with:
- Scanned W-2s, 1099s, and other IRS forms
- Brokerage statements saved as image scans
- Bank statements from older institutions
- Faxed or photocopied tax documents
Bundled Script
This skill includes scripts/pdf_to_images.py which handles PDF processing.
Workflow
Step 1: Install Dependencies
The bundled script uses uv run to manage its own dependencies (including pymupdf) automatically. No separate install step is needed — uv is already available on this system.
Run a quick check to confirm:
uv run --version
If uv is not found, fall back to installing pymupdf manually:
python -m pip install pymupdf
Step 2: Detect Scanned PDFs
When filesystem.read_document returns an error like "no text could be extracted" or "scanned/image-only", the PDF is likely image-based.
Step 3: Try Native Text Extraction First
Use the bundled script's extract mode to check whether the PDF has an embedded text layer. This is not OCR — it reads text that is already encoded in the PDF, which succeeds for digitally-created PDFs but fails for pure scans:
uv run <skill_dir>/scripts/pdf_to_images.py extract "<pdf_path>"
Check the has_text field in the JSON output. If true, use the extracted text directly.
Step 4: Render Pages as Images
If text extraction yields no results, render the PDF pages as images for vision analysis:
uv run <skill_dir>/scripts/pdf_to_images.py render "<pdf_path>" "<output_dir>" --dpi 300
Important: Rendering large or high-DPI PDFs can take over 30 seconds. Use timeout_secs: 120 when running this command via shell.execute.
Where <output_dir> is a temporary directory for the images. For large PDFs, process specific pages:
uv run <skill_dir>/scripts/pdf_to_images.py render "<pdf_path>" "<output_dir>" --dpi 300 --pages 1-5
The output is a JSON object listing the generated image files:
{
"images": [
{"page": 1, "path": "/tmp/output/page_0001.png", "width": 2550, "height": 3300},
{"page": 2, "path": "/tmp/output/page_0002.png", "width": 2550, "height": 3300}
],
"total_pages": 10,
"rendered": 2
}
Step 5: Analyze with Vision
Read each rendered image using filesystem.read_file with the image paths from the JSON output. The images will be sent to the model as vision inputs, allowing you to read and interpret the scanned document content.
For tax documents, focus on extracting:
- All dollar amounts and their field labels
- Names, addresses, TINs/SSNs (note: report only last 4 digits for privacy)
- Form numbers and tax year
- Box numbers and their values
Step 6: Clean Up
After analysis, delete the temporary image files to save disk space.
Tips
- DPI setting: 300 DPI is the default and works well for most documents. Use 200 DPI for faster processing of large documents where fine detail isn't critical. Use 400+ DPI only if text is very small or blurry.
- Page selection: For multi-page PDFs, process only the pages you need rather than the entire document. Tax forms typically have key data on specific pages.
- Multi-step analysis: For complex documents, render and analyze a few pages at a time rather than all at once, to keep context focused.
- Cross-reference: After extracting data from a scanned document, cross-reference the values against other documents (e.g., W-2 Box 1 should match 1040 Line 1).
