docs(wiki): record fast-alpr as the evaluated ANPR recognizer baseline

Research note from the recognizer-options query. fast-alpr v0.4.0 (MIT) — a swappable
YOLOv9-detector + CCT-OCR pipeline on ONNX Runtime, CPU-only and offline — fits the
decided vision-service architecture and is MIT end-to-end (code + published weights),
so the ANPR path may not need the scoped AGPL exception. Flags the open caveats:
verify model-weight provenance, and benchmark AL-plate accuracy (default global vs.
the 40+ country EU model). fast-alpr is plate-only, so the vehicle-verification job
stays ours to build. Decision kept open. Updates opencv-anpr-service (new "Recognizer
evaluation" section + licensing nuance), vision-service (open/next), index, log.

Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
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## Open / next
- Recognizer + vehicle-model selection and accuracy targets; fingerprint method + anomaly
threshold ([[opencv-anpr-service]]).
- Recognizer selection: **fast-alpr (MIT, YOLOv9 detector + CCT OCR on ONNX Runtime) is the evaluated
baseline** as of 2026-06-19 — fits this decision's shape (separate offline localhost process,
swappable) and is permissive end-to-end, so the ANPR path may not need the AGPL exception (pending a
model-**weight-provenance** check). Plate-only: it does NOT cover the vehicle-verification job. Open:
the provenance check + an accuracy benchmark on real Albanian plates (default global vs. the 40+
country EU model). See [[opencv-anpr-service]] "Recognizer evaluation".
- Vehicle-model selection + fingerprint method + anomaly threshold (the Job-2 anti-spoofing stage,
still ours to build on the same ONNX runtime) ([[opencv-anpr-service]]).
- Appliance compute footprint (CPU vs. small GPU/NPU) — [[bom]] / [[open-questions]].
- Service API + the Node-side adapter; per-camera opt-in wiring.
- Reconciliation logic that consumes plate+vehicle witness vs. commanded opens (still unbuilt — see