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/config.py

@@ -31,9 +31,39 @@ class MotionConfig:
 @dataclass
 class DocumentConfig:
     min_area_ratio: float = 0.04
+    # Candidates covering more than this fraction of the frame are rejected
+    # as likely background/lighting rather than a document.
+    max_area_ratio: float = 0.92
+    # Candidates whose long side is more than this many times their short
+    # side are rejected (fixes thin sliver/edge-strip false detections).
+    max_aspect: float = 6.0
     canny_low: int = 50
     canny_high: int = 150
+    # Contour search runs on a frame downscaled to this width; edge/threshold
+    # gap-closing kernels are sized relative to it rather than the source
+    # resolution, and the winning quad is scaled back up for the actual crop.
+    # Verified fix: a 4K frame's document outline was fragmenting into
+    # sub-regions with a fixed-pixel kernel tuned for ~1000px-wide frames.
+    detect_width: int = 1000
+    # Bake illumination-normalization + contrast enhancement into the saved
+    # crop/PDF, not just the internal OCR pass (which always applies it).
     enhance: bool = False
+    # Auto-detect and correct 0/90/180/270 rotation before OCR/PDF export.
+    # Fixes the "placed the document upside down" case, which otherwise
+    # produces a fine-looking crop that OCR reads as garbage.
+    auto_rotate: bool = True
+    # Tighten each crop to the actual paper region (removes mat margins and
+    # hands hanging off the side of receipts) and inpaint fingers pressing
+    # on the page. Content *under* a finger can't be recovered — it's filled
+    # with plausible paper texture — but margins clean up completely.
+    refine: bool = True
+    remove_fingers: bool = True
+    # Frame-based capture: cluster consecutive per-frame detections into
+    # separate documents when no contour is seen for this many seconds.
+    event_gap_s: float = 2.0
+    # Require at least this many sampled frames with a detection before
+    # committing a capture event (filters single-frame noise).
+    event_min_samples: int = 2
 
 
 @dataclass
@@ -65,6 +95,17 @@ class OcrConfig:
     min_word_confidence: float = 10.0
 
 
+@dataclass
+class LlmConfig:
+    # Use a local Ollama vision model to identify/name documents and flag
+    # quality issues. Falls back to Tesseract heuristics when Ollama is
+    # down or the model errors. ~20-40s per document vs <2s for Tesseract.
+    enabled: bool = True
+    model: str = "qwen2.5vl"
+    base_url: str = "http://localhost:11434"
+    timeout_s: float = 180.0
+
+
 @dataclass
 class NamingConfig:
     max_vendor_len: int = 40
@@ -84,6 +125,7 @@ class Config:
     document: DocumentConfig = field(default_factory=DocumentConfig)
     speech: SpeechConfig = field(default_factory=SpeechConfig)
     ocr: OcrConfig = field(default_factory=OcrConfig)
+    llm: LlmConfig = field(default_factory=LlmConfig)
     naming: NamingConfig = field(default_factory=NamingConfig)
     output: OutputConfig = field(default_factory=OutputConfig)