# 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. Detection scores candidate contours by shape
   (filled, convex, plausible aspect ratio) rather than just picking the
   largest one, and runs on a resolution-independent downscale so it works
   the same on FHD and 4K phone video.
3. The crop is auto-rotated upright (0/90/180/270) — no need to place
   documents facing a particular way.
4. Tesseract OCR (run against an illumination-normalized "flattened" version
   of the crop, similar to a scanner app's contrast enhancement) extracts a
   vendor/date/total for receipts, or a form code + organization name for
   recognized Canadian tax slips (T4, T4A, T5008, ...), and names the PDF.
5. (Upcoming) Spoken descriptions (faster-whisper) as an alternate naming
   source; multi-page stacks grouped into single PDFs; a review step to
   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`).

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

## Usage

```bash
# 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
  `<form_code>_<org_name>` for recognized tax slips), 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/form_code/date/total/confidence, detection
  confidence, rotation applied, final filename

Before naming, each crop is auto-rotated to be right-side-up (Tesseract's
built-in orientation detection, falling back to a 4-way OCR-confidence sweep
when a crop has too little text for that to work) — you don't need to worry
about which way documents face when placing them.

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/shape filters, Canny thresholds, detection
downscale width, auto-rotate/enhance toggles, 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.

**Recording tips (from real-world testing):** shoot in 4K, keep hands out
of frame once a document is placed, and use a plain dark, matte (non-glossy)
surface under documents — busy wood grain or shiny surfaces make contour
detection and OCR meaningfully harder. Document detection and orientation
correction are tuned against real phone-camera footage (receipts on a wood
table and multi-page CRA tax slips), not just synthetic test videos.

## Tests

```bash
python -m pytest
```

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