varunr89/claude-marketplace/video-ocr

Video OCR

Extracts frames from a video at a configurable FPS using ffmpeg, optionally deduplicates visually similar frames using perceptual hashing (aHash), then runs Apple Vision OCR on each kept frame in parallel.

When to use

Use this skill when the user wants to extract text from a video -- for example lecture recordings, tutorial screencasts, or presentation recordings where on-screen text changes over time.

Usage

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/video_ocr.py <video_file> \
  [-o output.jsonl] \
  [--fps 3.0] \
  [--workers 8] \
  [--dedupe] [--dedupe-threshold 0.15] [--hash-size 8] \
  [--frames-out <dir>] \
  [--markdown <output.md>] [--markdown-images]

Key arguments

Pipeline

1. ffmpeg extracts frames from the video at the specified FPS
2. If --dedupe is enabled, frames are compared using average perceptual hash (aHash) and similar consecutive frames are dropped
3. Apple Vision OCR runs in parallel on all kept frames
4. Results are sorted by frame number and written to JSONL (one JSON object per frame with frame, time_sec, and text fields)
5. Optionally, a Markdown file is generated with frame headings and OCR text blocks

Deduplication

The deduplication feature uses average perceptual hashing (aHash):

• Each frame is resized to hash_size x hash_size grayscale
• Pixels above the mean are mapped to 1, below to 0
• Consecutive frames whose Hamming distance ratio is below --dedupe-threshold are dropped
• This efficiently removes near-duplicate frames (e.g., static slides)

Dependencies

• ffmpeg (brew install ffmpeg) -- frame extraction
• macOS + PyObjC (pip install pyobjc-core pyobjc-framework-Vision pyobjc-framework-Cocoa) -- Vision OCR
• Pillow (pip install pillow) -- required for --dedupe