pdf-ocr

name	pdf-ocr
description	Extract text from PDF files using Apple Vision OCR, optimized for Apple Silicon

PDF OCR

Converts PDF pages to images using PyMuPDF, then runs Apple Vision OCR on each page in parallel. Produces Markdown, plain text, or JSONL output.

When to use

Use this skill when the user wants to extract text from a PDF file -- especially scanned PDFs, image-based PDFs, or PDFs where copy-paste produces garbled text.

Usage

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/pdf_ocr.py <input.pdf> \
  [-o output_file] \
  [-f markdown|text|jsonl] \
  [--dpi 200] \
  [--workers N] \
  [--languages en-US] \
  [--fast] \
  [--keep-images] [--images-dir <dir>] \
  [--stdout]

Key arguments

Argument	Default	Description
pdf (positional)	required	Input PDF file path
-o, --output	<pdf_name>.md	Output file path
-f, --format	markdown	Output format: markdown, text, or jsonl
--dpi	200	Resolution for rendering PDF pages (higher = better quality, slower)
--workers	CPU count	Number of parallel OCR workers
--languages	en-US	Comma-separated recognition languages
--fast	false	Use faster, less accurate recognition
--keep-images	false	Keep the extracted page images after OCR
--images-dir	temp dir	Directory to save page images (requires --keep-images)
--stdout	false	Print extracted text to stdout instead of writing to file

Pipeline

1. PDF pages are rendered to PNG images at the specified DPI using PyMuPDF (fitz)
2. Apple Vision OCR runs in parallel across pages using ThreadPoolExecutor
3. Results are assembled in page order and written to the chosen output format

Output formats

• markdown -- Each page becomes a ## Page N section with the OCR text
• text -- Plain text with === Page N === separators
• jsonl -- One JSON object per line with page, text, and backend fields

Dependencies

• PyMuPDF (pip install pymupdf) -- PDF to image conversion
• macOS + PyObjC (pip install pyobjc-core pyobjc-framework-Vision pyobjc-framework-Cocoa) -- Vision OCR