Toji Doc Extractor

Comprehensively extract text, tables, images, and metadata from PDF and Word (.docx) documents. Use when a user shares a document and wants it parsed, analyzed, summarized, or processed. Handles native PDFs, scanned PDFs (via OCR), and Word documents.

doc-extract

Extracts everything useful from PDF and Word documents so you can process, summarize, or act on the content.

When to Use

• User shares or references a PDF or DOCX file
• Need to read, summarize, or analyze a document
• Extracting tables for data processing
• Pulling images out of a document
• Checking document metadata (author, date, title)
• OCR on scanned/image-based PDFs

Requirements

• Python venv: ~/.openclaw/venvs/doctools
• Libraries: pymupdf, pdfplumber, python-docx (pre-installed)
• CLI tools: tesseract (OCR), pandoc (optional), pdftotext (from poppler)
• All installed at: /opt/homebrew/bin/

Script

scripts/extract.py — the main extractor. Always use the venv:

~/.openclaw/venvs/doctools/bin/python3 /Users/kong/.openclaw/workspace/skills/doc-extract/scripts/extract.py <file> [options]

Options

Flag	Description
--format markdown	Human-readable output (default)
--format json	Structured JSON for programmatic use
--output-dir <dir>	Where to save extracted images (default: <filename>_extracted/ next to file)
--ocr	Run Tesseract OCR on pages with no extractable text (scanned docs)

Common Workflows

Extract and read a PDF

~/.openclaw/venvs/doctools/bin/python3 /Users/kong/.openclaw/workspace/skills/doc-extract/scripts/extract.py ~/Desktop/report.pdf

Extract a Word doc

~/.openclaw/venvs/doctools/bin/python3 /Users/kong/.openclaw/workspace/skills/doc-extract/scripts/extract.py ~/Desktop/contract.docx

Scanned PDF (OCR mode)

~/.openclaw/venvs/doctools/bin/python3 /Users/kong/.openclaw/workspace/skills/doc-extract/scripts/extract.py ~/Desktop/scan.pdf --ocr

Get structured JSON output

~/.openclaw/venvs/doctools/bin/python3 /Users/kong/.openclaw/workspace/skills/doc-extract/scripts/extract.py ~/Desktop/data.pdf --format json

Save images to a specific folder

~/.openclaw/venvs/doctools/bin/python3 /Users/kong/.openclaw/workspace/skills/doc-extract/scripts/extract.py ~/Desktop/report.pdf --output-dir ~/Desktop/report_images

Output Structure

Markdown mode gives you:

• Document metadata (title, author, date)
• Summary (page count, table count, image count)
• Full extracted text
• Tables rendered as Markdown tables
• Image paths for extracted images
• Any warnings/errors

JSON mode gives you:

• type: pdf or docx
• file: source path
• metadata: document properties
• text: full concatenated text
• pages_text: per-page text (PDF only)
• tables: array of {page, index, rows, cols, data[][]}
• images: array of {page, index, path, format, width, height}
• errors: any non-fatal issues encountered

Extraction Strategy

1. PDF native text → pymupdf (fast, accurate for digital PDFs)
2. PDF tables → pdfplumber (best-in-class table detection)
3. PDF images → pymupdf image extraction
4. PDF scanned pages → tesseract OCR (only triggered with --ocr or when page has no text)
5. DOCX text → python-docx (preserves paragraph styles/headings)
6. DOCX tables → python-docx table parser
7. DOCX images → extracted from document relationships

Model

Always use claude-opus-4-6 (opus) when analyzing extracted content — summarizing, answering questions, processing tables, etc.

Tips

• For large documents, use --format json and parse programmatically
• Images are saved to disk — reference the paths in follow-up tasks
• Tables come out as 2D arrays — easy to convert to CSV or analyze
• For password-protected PDFs, unlock first with: qpdf --decrypt input.pdf output.pdf
• For .doc (old Word format), convert first: pandoc input.doc -o output.docx