fix(identity): calibrate both accept lines against unrelated shelves

The gates were tuned on image6 alone, where every crop is a facing of a product
the shelf actually holds. Wikimedia supermarket photos of milk, soy and snack
shelves share nothing with the LOPIA catalog, so every accept there is a false
accept by construction - and at the old lines there were 83 of them.

Text lane: TEXT_MIN_SHARED 2 -> 4. The vocabulary filter drops bigrams no name
contains, so a short garbage read can end up with a perfect ratio on two
coincidental pairs. 20 of 26 text-lane answers on those shelves rested on
exactly two: '避くて おいしい' named sku_l12 through おい/いし.

Local lane: LOCAL_ACCEPT 2.0 -> 4.0. The residual accepts were geometric
matches with no text overlap at all, scoring 2.26-4.81.

Measured, 2297 distractor crops and 109 image6 facings:
  accept  floor   image6 shown/correct   precision   false accepts
  2.0     2       54 / 49                90.7%       83 (3.61%)
  2.0     4       52 / 48                92.3%       30 (1.31%)
  4.0     4       44 / 42                95.5%        8 (0.35%)
  5.0     6       36 / 36               100.0%        0 (0.00%)

4.0/4 is the only region passing both G2 (>=95% precision) and G3 (<=1%
far-unknown) with coverage left. On the shipped evaluator: 66% end-to-end at
95% precision, 2 false accepts, against 77%/91%/5 before.

The two tests that pinned the old floors now pin the new ones, with the reads
that separate them.

recognization/code/identity_lanes.py

@@ -43,7 +43,14 @@ from rebuild_catalog import bigrams, normalize_text, signature_bigrams, sku_of
 # on the corrected image6 labels: 2.0 keeps 95% of shown labels correct at 59%
 # coverage, 1.0 keeps 87% at 66%. The stricter line is the default because a
 # wrong label on a shelf is worse than a missing one.
-LOCAL_ACCEPT = 2.0
+#
+# 4.0 since 2026-09-29: the line was tuned on image6 alone, where every crop is
+# a facing of a product the shelf actually holds. On 2297 crops from unrelated
+# Wikimedia shelves, 2.0 accepted 25 of them on geometry alone (scores 2.26-4.81
+# with no text overlap at all), a 1.1% far-unknown rate against G3's 1%. At 4.0
+# that falls to 8 (0.35%) while image6 keeps 42 of 48 correct labels at 95.5%
+# precision, which is the only region passing both G2 and G3 with coverage left.
+LOCAL_ACCEPT = 4.0
 # The text lane's own accept line: a bigram overlap this high, with this much
 # margin over the runner-up, names the row on its own. Measured: the text lane
 # picks the right SKU on 41-42 of the 55 facings where it reads anything.
@@ -51,12 +58,16 @@ TEXT_ACCEPT = 0.5
 TEXT_MARGIN = 0.15
 # How many of the read's bigrams the winning row must actually contain. The
 # ratio alone is not evidence: a two-pair read the row happens to contain scores
-# 1.0, and "サソース" named sku_l08 that way. Two pairs is the floor that keeps
-# every correct text answer on image6 (40 of 40) while dropping the wrong ones
-# from 6 to 2, taking shown-label precision from 87% to 95%. It is a floor on
-# evidence, not on the ratio, so a long read of shared brand copy still scores
-# low and is still refused by the margin.
-TEXT_MIN_SHARED = 2
+# 1.0, and "サソース" named sku_l08 that way. Two pairs was the old floor, and it
+# is too low once the lane answers on unrelated shelves: on 2297 crops from
+# Wikimedia milk, soy and snack shelves, 20 of 26 text-lane answers rested on
+# exactly two shared bigrams ("避くて おいしい" -> sku_l12 via おい/いし), an
+# 11.4% false accept rate against G3's 1%. Four is where the two sides separate
+# once the local line is also at 4.0: false accepts fall to 8 (0.35%) while
+# image6 keeps 42 of its 48 correct labels. It is a floor on evidence, not on
+# the ratio, so a long read of shared brand copy still scores low and is still
+# refused by the margin.
+TEXT_MIN_SHARED = 4
 # Reference pyramid heights. A ~1000 px packshot against a 110-250 px crop is a
 # 4-8x ratio, and ORB's 8x1.2 pyramid tops out at 3.6x, which is why ORB and
 # BRISK find zero inliers at live resolution. Scaling each reference to the