Improve identity lanes: text evidence floor, motion-compensated labels

The text lane now requires shared bigrams between the read and the winning
row's name before it will answer, and the local lane passes the catalog
signatures through so the decision can weigh text evidence. On the labeled
shelf this moves the local lane from 66% to 80% end-to-end at full
resolution (91% precision) and 66% at live resolution (95% precision, 2
false accepts), against 57%/35% for the matcher alone.

Live fixes:
- `_plan_classification` counts `in_flight` again. The increment had been
  dropped while removing a `label_frame_id` write, so the guard never
  blocked, every frame re-planned the same tracks, and the executor queue
  grew without bound until the server stopped answering.
- Label memory boxes carry the shelf origin they were measured against and
  are shifted before the IoU test, so a panning camera no longer slides a
  stale box onto its neighbour.
- The text lane is resolved before the session lock is taken, removing a
  lock-ordering hazard against the lane's own build lock.
- A native crop always reads; the frame crop skips OCR only when it already
  has a reading.

Stream (20 s pan, 4 runs): 71-79% coverage at 76-84% precision, against
62-71% at 73-75% before.

recognization/code/engine.py

@@ -520,7 +520,9 @@ class RecognitionEngine:
             text_rank = sorted(
                 text_lane.scores_from_text(read).items(), key=lambda kv: kv[1], reverse=True
             )
-        return DecisionPolicy().decide(lane.rank(crop), text_rank, text_read=read)
+        return DecisionPolicy(signatures=text_lane.signatures if text_lane else None).decide(
+            lane.rank(crop), text_rank, text_read=read
+        )
 
     def score_crop_lanes(
         self, crop: Any, method: Optional[str] = None, skip_ocr: bool = False