docs(wiki): vision fitness assessment for entry/exit flows

Record the verdict: the ANPR service is worthy to consume NOW as an advisory plate
IDENTITY source (Job 1) — the flows already treat a kind:"plate" read as first-class
(exit signs source:"lpr"; subscription matches read plate vs bound plates), so it feeds
an existing input with no flow rewrite. It is NOT worthy as the sole authority to open a
transient barrier (a plate is not a payment; spoofing needs Job 2 vehicle verification,
unbuilt) — gated by the confidence floor with ticket/manual fallback. Lists the four
gaps before consumption (VisionClient adapter, opt-in trigger, field accuracy,
weight-provenance). Next step is the adapter, not more model work.

Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V

wiki/log.md

@@ -920,3 +920,7 @@ Scaffolded the [[opencv-anpr-service|vision service]] per [[vision-service-packa
 ## [2026-06-19] query | Albanian-plate OCR benchmark — keep the default (cct-xs-v2-global)
 
 Benchmarked fast-alpr's four candidate fast-plate-ocr models via the FULL pipeline (YOLOv9 detect → OCR) on real AL plate photos (Wikimedia: AA558EE, AA687KE), CPU, scaffolded apps/vision service. ALL FOUR read both plates correctly; the differentiator is confidence + speed: cct-xs-v2-global (default) 0.999/1.000 @ 33–39ms AND returns region=Albania; cct-s-v2-global same accuracy ~50% slower; global-mobile-vit ~0.955 fast; european-mobile-vit-v2 (the "40+ country EU" model) correct but MUCH lower confidence (~0.77) and misread a synthetic AB123FG→AB123FO. FINDING (overturns the "EU model → better for AL" assumption from the prior research turn): the global cct-xs default WINS for Albania — most accurate AND fastest. Decision: no config change, VISION_OCR_MODEL stays cct-xs-v2-global-model. Caveat: test photos were clean head-on shots; real booth captures (angle/night/dirt/blur) will lower confidence — the min_confidence=0.5 floor → low_confidence → ticket-path fallback covers it; re-benchmark on on-site captures once cameras installed. Resolves the AL-accuracy-benchmark open item in [[opencv-anpr-service]] (added a results table + the keep-default finding); the weight-provenance check remains the one open recognizer item.
+
+## [2026-06-19] query | Vision service fitness for entry/exit flows — advisory YES, sole-authority NO
+
+Q: is the scaffolded ANPR service worthy to consume in entry/exit flows? Assessment recorded in [[opencv-anpr-service]] ("Fitness for the entry/exit flows"). Benchmark settled ACCURACY (0.99+ clean AL plates); "worthy" turns on AUTHORITY. Split verdict: (✅) worthy NOW as an ADVISORY identity source (Job 1) — the flows are ALREADY built for a plate (kind:"plate" read is first-class: exit-flow signs source:"lpr"; subscription-flow matches read plate vs subscriptionPlates), so the service just produces the plate string → DeviceReadEvent{kind:"plate"} on the existing read bus; no flow rewrite. Worthy for hands-free subscriber open + evidence enrichment. (⚠️) NOT worthy as SOLE AUTHORITY to open a TRANSIENT barrier: a plate ≠ payment (would be an unpaid-exit bypass; min_confidence floor → ticket/manual fallback is the guard) and plate-spoofing (printed plate, different car) needs Job 2 vehicle-verification which is NOT built. Gaps before consuming: (1) the Node VisionClient adapter (real integration work), (2) trigger wiring — snapshots fire AFTER open today (evidence); plate-as-identity needs a snapshot BEFORE the decision on a per-camera opt-in lane, (3) field accuracy unknown (re-tune threshold on on-site captures), (4) weight-provenance check. Next step: VisionClient adapter + opt-in trigger, not more model work. (Scaffolding VisionClient next.)