PaperPod/README.md

PaperPod

Local, privacy-first tool that converts a single overhead video recording of documents (receipts, letters, multi-page stacks) into individual, properly named PDFs ready for a Paperless-ngx consume folder. All processing runs offline on your machine.

How it works

1. Record one continuous overhead video, placing documents under the camera one at a time (optionally saying out loud what each one is).
2. PaperPod finds "stable windows" where nothing is moving, picks the sharpest frame in each, detects the document outline, and produces a perspective-corrected crop.
3. (Upcoming) Spoken descriptions (faster-whisper) or OCR (Tesseract) name each document; multi-page stacks are grouped into single PDFs; a review step lets you rename/merge/split before export.

Status

Module	Purpose	Status
capture/	Frame sampling + audio extraction from video files	Built
vision/	Motion detection, document contours, perspective crop, sharpness	Built
events/	State machine: placed / page-flipped / cleared, pod grouping	Planned
transcribe/	Local speech-to-text (faster-whisper)	Planned
ocr/	OCR fallback naming (Tesseract): vendor, date, total	Built
naming/	Final filename assembly + summary CSV	Built
pdf/	PDF assembly (img2pdf)	Built — single-page only, no consume-folder staging
review_cli/	Pre-export review (rename/merge/split)	Planned

Known limitation: there is no page-flip / multi-page grouping yet (that's events/). Every detected document currently becomes its own single-page PDF, even if it was physically one page of a stack or one side of a double-sided document. Don't point export at the Paperless-ngx consume folder for multi-page documents until events/ exists — you'll get one PDF per page instead of one PDF per document.

Setup

Requires Python 3.11+ and ffmpeg (brew install ffmpeg).

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Usage

# Generate a synthetic test video (no camera needed)
python sample_data/make_sample_video.py

# Inspect a video
python -m paperpod probe sample_data/videos/synthetic_sample.mp4

# Detect stable windows + document candidates
python -m paperpod detect sample_data/videos/synthetic_sample.mp4

# Detect, OCR-name, and export each document as its own PDF
python -m paperpod export sample_data/videos/receipts_sample.mp4

# Extract the audio track (16 kHz mono WAV, whisper-ready)
python -m paperpod extract-audio my_recording.mp4

detect writes to output/<video-name>/:

• crops/window_NNN.png — perspective-corrected document candidates
• frames/window_NNN_full.png — the full best frame per window (debugging)
• report.json — video metadata, motion events, stable windows, detections
• motion_scores.csv — per-sample motion scores, for tuning motion.threshold

export runs detect and then, for every detected document, additionally writes:

• pdf/<name>.pdf — one single-page PDF per detected document, named YYYY-MM-DD_vendor.pdf from OCR-extracted date/vendor, or UNSORTED_<timestamp>.pdf if OCR couldn't find anything usable
• export_summary.csv — timestamp, pod_id, page_count, source_of_name (ocr/none), OCR vendor/date/total/confidence, final filename

Nothing is copied into a Paperless-ngx consume directory yet — review the pdf/ folder yourself before moving files anywhere.

Tuning

All thresholds live in config.yaml (motion sensitivity, stable window duration, contour size floor, Canny thresholds, speech matching window, output paths). Plot motion_scores.csv to pick a motion.threshold that separates your camera's noise floor from real hand movement.

Tests

python -m pytest

Tests are self-contained: they synthesize frames and tiny videos on the fly, no sample assets required.