Add crop refinement, finger removal, and local LLM naming

- vision/refine.py: tighten crops to the paper band (removes mat
  margins and hands beside receipts) and inpaint border-connected
  skin regions so fingers disappear from output
- llm/vision.py: identify documents with a local Ollama vision model
  (qwen2.5vl); extracts vendor/date/total/form code and flags quality
  issues (fingers, blur, glare); falls back to Tesseract when down
- pipeline: drop blank pages, dedupe consecutive captures of the same
  document, record refine/LLM fields in report and export summary

config.yaml

@@ -27,11 +27,45 @@ motion:
 document:
   # Contours smaller than this fraction of the frame area are ignored.
   min_area_ratio: 0.04
+  # Contours larger than this fraction of the frame are rejected as likely
+  # background/lighting rather than a document.
+  max_area_ratio: 0.92
+  # Reject candidates whose long side is more than this many times their
+  # short side (filters out thin slivers/edge-strip false detections).
+  max_aspect: 6.0
   # Canny edge detection thresholds.
   canny_low: 50
   canny_high: 150
-  # Apply contrast enhancement (CLAHE) to crops for OCR-readiness.
+  # Contour search runs on a frame downscaled to this width (then the
+  # winning quad is scaled back up for the full-resolution crop). Keeps
+  # edge/gap-closing behavior consistent regardless of source resolution —
+  # important for 4K phone video.
+  detect_width: 1000
+  # Bake illumination-normalization + contrast enhancement into the saved
+  # crop/PDF (grayscale "flattened scan" look). OCR always uses this
+  # enhancement internally regardless of this setting.
   enhance: false
+  # Auto-detect and correct 0/90/180/270 rotation before OCR/PDF export.
+  auto_rotate: true
+  # Tighten crops to the actual paper region (cuts mat margins/hands) and
+  # inpaint fingers pressing on the page.
+  refine: true
+  remove_fingers: true
+  # Split separate document placements when no contour is seen for this long.
+  event_gap_s: 2.0
+  # Ignore single-frame detection blips.
+  event_min_samples: 2
+
+llm:
+  # Identify/name documents with a local Ollama vision model (all offline).
+  # Reads whole documents at once — far more robust than Tesseract for
+  # curled receipts and stylized logos — and flags quality issues
+  # (fingers_visible, blurry, glare) in export_summary.csv.
+  # Slower: ~20-40s per document. Falls back to Tesseract when Ollama is down.
+  enabled: true
+  model: qwen2.5vl
+  base_url: http://localhost:11434
+  timeout_s: 180
 
 speech:
   # Window around a capture event in which to look for a spoken description.