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

paperpod/vision/enhance.py

@@ -1,4 +1,12 @@
-"""Optional image enhancement for OCR-readiness."""
+"""Scan-style image enhancement, for OCR-readiness and optionally the saved crop.
+
+`normalize_illumination` is the "flatten it, make it easier to read" trick
+scanner apps like CamScanner use: divide the image by a heavily-blurred copy
+of itself (an estimate of the local lighting/shadow), which cancels out
+gradients, glare, and wood-grain/shadow texture far better than a plain
+contrast boost. Confirmed on a real low-contrast receipt-on-wood-grain photo:
+raw OCR read almost nothing, normalized OCR recovered full lines of text.
+"""
 
 from __future__ import annotations
 
@@ -6,15 +14,25 @@ import cv2
 import numpy as np
 
 
-def enhance_for_ocr(image_bgr: np.ndarray) -> np.ndarray:
-    """Boost local contrast (CLAHE on the luminance channel).
+def normalize_illumination(image_bgr: np.ndarray, blur_fraction: float = 1 / 15) -> np.ndarray:
+    """Cancel out uneven lighting/shadow/background texture. Returns grayscale."""
+    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
+    sigma = max(image_bgr.shape[1] * blur_fraction, 10.0)
+    background = cv2.GaussianBlur(gray, (0, 0), sigmaX=sigma)
+    # Background approaches white; dividing pulls the true page background
+    # up to ~255 wherever lighting made it dim, while text (locally much
+    # darker than its surroundings) stays dark.
+    return cv2.divide(gray, background, scale=255)
 
-    Deliberately conservative: no binarization, so the crop stays pleasant to
-    read in the PDF while giving OCR more to work with. Thresholding can be
-    added behind a config flag later if OCR needs it.
+
+def enhance_for_ocr(image_bgr: np.ndarray) -> np.ndarray:
+    """Illumination-normalize + local contrast boost. Returns a BGR image.
+
+    Always grayscale-looking (3 identical channels) since normalization
+    operates on luminance. Intended primarily as an OCR preprocessing step;
+    only baked into the saved crop/PDF when document.enhance is enabled.
     """
-    lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
-    l_chan, a_chan, b_chan = cv2.split(lab)
+    normalized = normalize_illumination(image_bgr)
     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-    lab = cv2.merge((clahe.apply(l_chan), a_chan, b_chan))
-    return cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
+    boosted = clahe.apply(normalized)
+    return cv2.cvtColor(boosted, cv2.COLOR_GRAY2BGR)