docs(wiki): Albanian-plate OCR benchmark — keep cct-xs-v2-global default

Benchmarked fast-alpr's four fast-plate-ocr models via the full pipeline on real AL
plates (AA558EE, AA687KE), CPU. All four read both correctly; the default
cct-xs-v2-global-model wins on confidence (0.999/1.000) AND speed (33-39ms) and returns
region=Albania. The "European 40+country" model is WORSE here (~0.77 confidence, one
synthetic misread) — overturning the "EU model better for AL" assumption from the prior
research. Decision: no config change. Resolves the AL-accuracy-benchmark open item
(results table + finding added to opencv-anpr-service); weight-provenance remains the
one open recognizer item. Re-benchmark on real on-site captures once cameras installed.

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

wiki/log.md

@@ -916,3 +916,7 @@ Q: how to IMPLEMENT the vision service — can we use this Turborepo? Settled (s
 ## [2026-06-19] build | Scaffold apps/vision (ANPR microservice skeleton)
 
 Scaffolded the [[opencv-anpr-service|vision service]] per [[vision-service-packaging]]: `apps/vision/` Python/FastAPI, uv-managed, wired into Turbo via a thin package.json shim. Structure: pyproject.toml (light core: fastapi/uvicorn/pydantic; HEAVY recognizer = optional `alpr` extra = fast-alpr+onnxruntime, so `uv sync`+tests run OFFLINE in stub mode with no model download), per-package turbo.json (extends ["//"], build outputs [] → warning-free no-op), .gitignore (venv/caches/*.onnx/models out). vision_service/: app.py (GET /health + POST /analyze, raw octet-stream body so Node POSTs Snapshot.bytes directly; empty→400, oversize→413, not-ready→503), settings.py (env VISION_*), schemas.py (the /analyze contract + a not-yet-populated `vehicle` field for Job 2), recognizer.py (a Recognizer Protocol + StubRecognizer/FastAlprRecognizer — the device-adapter pattern applied to the model; fast-alpr imported lazily so missing models ⇒ ready=False, not a crash). VERIFIED: turbo run lint|test|build includes @parking/vision (ruff/pytest/no-op shim) green; uv run mypy strict-clean; uvicorn boots + serves /health (ready, stub-0) and /analyze (contract shape) live; pnpm workspace 6→7. NOT built: the Node VisionClient adapter, a Dockerfile + model fetch, and Job 2 (vehicle verification). Updated [[vision-service-packaging]] (As-scaffolded section), CLAUDE.md layout already lists apps/vision.
+
+## [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.