docs(decision): date the header and re-measure the OCR cost

The header said 'Audited 2026-09-28' while the document now carries 2026-09-29
implementation results; it says both, and that audit-time figures are kept
beside the later ones rather than replaced.

The OCR cost row claimed 18.0 ms on 40 production crops with no committed
script behind it. code/bench_ocr.py is that measurement: 23.3 ms p50, 37.2 ms
p95, mean 23.1 - inside the recorded 18-26 ms band, at its upper half.

recognization/code/bench_ocr.py

@@ -0,0 +1,42 @@
+"""OCR cost per crop, on production-shaped crops.
+
+The decision record claims ~18-26 ms per crop on Mac and re-measured 18.0 ms on
+40 production crops. This is that measurement, so the number is reproducible
+rather than remembered.
+
+    .venv/bin/python code/bench_ocr.py
+"""
+import json, statistics, sys, time
+sys.path.insert(0, 'code')
+import cv2
+from eval_identity import LocalLaneAdapter, crop_of
+
+lane = LocalLaneAdapter('stock-lopia-clean')
+labels = json.load(open('eval/labels/image6.json'))
+image = cv2.imread('add/image6.jpg')
+
+crops = []
+for facing in labels['facings']:
+    if facing.get('label') in (None, 'exclude'):
+        continue
+    crop = crop_of(image, facing['box'])
+    if crop is not None and crop.size:
+        crops.append(crop)
+    if len(crops) >= 40:
+        break
+
+# Warm the recognizer: the first call loads the Vision model.
+lane.text_lane.read(crops[0])
+
+times = []
+for crop in crops:
+    started = time.perf_counter()
+    lane.text_lane.read(crop)
+    times.append((time.perf_counter() - started) * 1000)
+
+times.sort()
+print(f'{len(times)} crops')
+print(f'  p50 {statistics.median(times):.1f} ms')
+print(f'  mean {statistics.mean(times):.1f} ms')
+print(f'  p95 {times[int(len(times) * 0.95)]:.1f} ms')
+print(f'  min {times[0]:.1f}  max {times[-1]:.1f}')