---
type: entity
tags: [parking, vision, anpr, anti-fraud, service]
sources: []
updated: 2026-06-15
status: open
---

# OpenCV ANPR / Vision Service

A **local microservice** that analyses camera snapshots: reads the licence **plate** (ANPR) and
extracts **vehicle attributes** for verification. Built by us (decision 2026-06-15) to do
recognition **host-side on ordinary IP-camera snapshots**, replacing the dedicated edge-AI
[[lpr-camera]]. See decision [[vision-service]].

## Two jobs

1. **Identity (ANPR).** snapshot → `{ plate, confidence, bbox }`. Feeds the existing
   `IdentitySource = "lpr"` ([[parking-session]]): the plate is a session/identity key and the way
   a plate-bound [[subscription]] is matched.
2. **Verification (anti-fraud witness).** snapshot → vehicle attributes — at minimum
   `{ make?, model?, colour, bodyType }`, ideally a compact **visual fingerprint** (an embedding).
   This is the answer to **plate-spoofing**: *a fraudster prints a registered/paid plate and drives
   in with a different car.* Plate-reading alone can't catch that; comparing the **vehicle** seen at
   entry vs. exit (and vs. the [[subscription]]'s known car) can. A plate that entered on a red hatchback
   but exits on a black SUV is a **reconciliation anomaly** — exactly the independent-witness role
   the [[append-only-event-chain]] flags as the unbuilt gap. See [[reconciliation]].

> The two jobs are why this is worth building rather than just plate-OCR: the service is both an
> **identity source** and an **independent witness**, the visual analogue of the whole system's
> "two records that must reconcile" thesis.

## Architecture — separate localhost process

- A **Python service** (e.g. FastAPI) running **on the appliance**, called by the Node backend over
  **localhost HTTP** (`POST /analyze` with the JPEG bytes the camera driver already pulls — see
  [[lpr-camera]] "driver/storage boundary": `Snapshot.bytes`). **Source lives in this monorepo at
  `apps/vision/`** (Turbo shim; `uv`-managed deps) — co-located source, separate process; see
  [[vision-service-packaging]].
- **Fully offline** ([[offline-first]]): all inference is local, no cloud. Model weights ship on the
  appliance.
- **Process isolation is deliberate** — it keeps a heavy Python/native/AGPL stack out of the
  Node app's process and license surface (see licensing below), and gives it its own failure
  domain. If the service is down/slow, the host falls back (transient ticket path) rather than
  blocking the lane.
- **Request/response (first cut):**
  - `POST /analyze` → `{ plate: {text, confidence, bbox}|null, vehicle: {colour, bodyType, make?, model?, embedding?}, modelVersion, tookMs }`
  - `GET /health` → readiness + model versions.
- The Node side wraps it behind an internal interface (like a device adapter) so the recognizer can
  be swapped without touching business logic.

## Licensing — scoped AGPL exception (amends the standing rule)

The app is strictly **MIT/Apache/BSD** ([[technology-stack]], [[standing-decisions]]). Accurate
ANPR/vehicle models were *assumed* to be mostly **AGPL** (Ultralytics YOLO detectors, OpenALPR) or
commercial — but the **fast-alpr stack (above) is MIT end-to-end**, so a permissive ANPR baseline now
looks achievable (pending the weight-provenance caveat). The exception below still matters for the
*strongest* models (Ultralytics YOLO) and for the vehicle-verification job. Decision (2026-06-15):
**allow AGPL inside this service only.** It is a **separate process**, not
linked into the app, so its obligations don't reach the Node/React codebase; the app's permissive
guarantee is preserved. Recorded as an explicit exception in [[standing-decisions]] /
[[vision-service]].

- OpenCV core itself is **Apache-2.0** (clean either way).
- AGPL note: if the appliance is ever offered as a network service to third parties, AGPL's
  network-use clause could require offering the service's source — relevant only if productised
  beyond the on-site appliance; flag at that point.

## Recognizer evaluation — fast-alpr is the leading baseline (2026-06-19)

`YOLO vs OpenCV` is a **category error** — they're different pipeline layers, not competitors. ANPR
is a **pipeline**: (1) plate **detection** (find the box → YOLO-family detector), (2) plate **OCR**
(read the crop → a CRNN/CCT or OCR engine), (3) **glue** (capture/crop/deskew/draw → OpenCV,
Apache-2.0, always present). So the real choice is *which end-to-end recognizer*, and **OpenCV is
used regardless** as the image-handling toolkit.

**Leading option: [fast-alpr](https://github.com/ankandrew/fast-alpr) (v0.4.0, 15 Mar 2026).** A thin
orchestrator over two **swappable** stages, both on **ONNX Runtime** — which matches THIS service's
decided architecture (separate localhost Python process, offline, swappable behind an interface)
almost exactly:

| Stage | Default model | Library | License |
| --- | --- | --- | --- |
| Plate detection | `yolo-v9-t-384-license-plate-end2end` | [open-image-models](https://github.com/ankandrew/open-image-models) | MIT |
| Plate OCR | `cct-xs-v2-global-model` | [fast-plate-ocr](https://github.com/ankandrew/fast-plate-ocr) | MIT |

- **MIT top-to-bottom** (library *and* the published model weights), one maintainer (ankandrew) across
  all three repos. **The detector is open-image-models' own YOLOv9 ONNX export — NOT the Ultralytics
  AGPL package** — so fast-alpr is a **permissive baseline that may not even need the scoped AGPL
  exception** below. ⚠️ **Caveat (verify before relying on it):** a repo's LICENSE covers its *code*;
  redistributed model *weights* can carry separate provenance (YOLOv9 upstream is GPL-3.0; Ultralytics
  YOLO is AGPL). Confirm the weight training/provenance (model card) before treating "MIT weights" as
  settled for compliance — the AGPL-in-service exception is the safety net if it doesn't hold.
- **CPU-only + fully offline.** No runtime ships by default; pick a backend extra — `fast-alpr[onnx]`
  (CPU), or `[onnx-gpu]`/`[onnx-openvino]`/`[onnx-directml]`/`[onnx-qnn]` — which maps onto the
  "CPU now, small GPU/NPU later" compute question ([[bom]], [[open-questions]]).
- **Albanian/EU plates:** fast-plate-ocr also has a **European model trained on 40+ countries** (newer
  than the default global model) — benchmark it against the default for AL accuracy.
- **Modular, no lock-in:** swap either stage via `BaseDetector`/`BaseOCR` (their docs plug in
  Tesseract). So fast-alpr is the baseline you keep while replacing one stage if needed.

**Scope: fast-alpr is plate-only — it does Job 1 (ANPR) but NOT Job 2 (vehicle verification).** The
anti-spoofing vehicle-attribute/fingerprint stage is still ours to build — but since fast-alpr already
standardizes on **ONNX Runtime + a YOLO-family detector**, the vehicle stage shares that runtime (the
coherent outcome). Other options, weaker: **OpenALPR** (permissive but largely unmaintained, the old
"permissive-only, weaker" path); **Ultralytics YOLO + PaddleOCR** (most accurate/tunable, but YOLO is
AGPL → needs the in-service exception; most build effort — the "scale" path if fast-alpr's accuracy
disappoints).

**Recommendation:** prototype with **fast-alpr** now (permissive, offline, ONNX, fits the decided
shape); plan a YOLO-detector fine-tune + PaddleOCR only if production accuracy demands it. Choice kept
**open** pending the weight-provenance check (the AL-plate benchmark below is now done).

### Albanian-plate OCR benchmark — keep the default (2026-06-19)

Ran the four candidate `fast-plate-ocr` models through the **full pipeline** (YOLOv9 detect → OCR) on
real AL plate photos (Wikimedia), CPU, scaffolded service:

| OCR model | `AA 558 EE` | `AA 687 KE` | Speed | Note |
| --- | --- | --- | --- | --- |
| **`cct-xs-v2-global-model`** (default) | ✓ 0.999 | ✓ **1.000** | **33–39 ms** | best accuracy + fastest; returns `region=Albania` |
| `cct-s-v2-global-model` | ✓ 0.998 | ✓ 0.999 | 50–65 ms | as accurate, ~50% slower |
| `global-plates-mobile-vit-v2-model` | ✓ 0.955 | ✓ 0.959 | 33–35 ms | fast, lower confidence |
| `european-plates-mobile-vit-v2-model` | ✓ 0.784 | ✓ 0.766 | 38–46 ms | correct but **much lower confidence**; misread a synthetic `AB123FG`→`AB123FO` |

**Finding (overturns the prior assumption):** the **default `cct-xs-v2-global-model` is the best for
Albania** — most accurate AND fastest. The "European (40+ country)" model is *worse* here (~0.77 vs
~1.0 confidence, one synthetic misread), despite the "EU model → better for AL" intuition. So **no
config change**: `VISION_OCR_MODEL` stays `cct-xs-v2-global-model`. Caveat: both test photos were
clean head-on shots; real booth captures (angled, dirty, night, motion-blur) will lower absolute
confidence — the `min_confidence=0.5` floor (→ `low_confidence` → ticket-path fallback) covers that.
The ranking should hold; re-benchmark on real on-site captures once the cameras are installed.

## Anti-fraud / threat-model fit

- **Plate spoofing** (the motivating case): vehicle-attribute / fingerprint mismatch entry↔exit or
  vs. a [[subscription]]'s registered car → anomaly. Doesn't *block* on its own (recognition is
  probabilistic) — it **flags for [[reconciliation]]** and is captured in the signed record.
- The recognition result and the source image both attach to the signed [[append-only-event-chain]]
  entry, so the *evidence* is tamper-evident even though recognition itself is host-side and
  fallible.
- Recognition is **advisory, never the sole authority** to open a barrier where money/access is at
  stake — confidence thresholds + fallback to ticket/manual; a low-confidence read must not strand a
  car ([[fail-state-safety]]).

## Open

- **Recognizer choice** — **fast-alpr (MIT, YOLOv9+CCT on ONNX) is the baseline, AL-benchmarked**: the
  default `cct-xs-v2-global-model` won over the EU model on real AL plates (table above). The one
  remaining open item is the **model-weight-provenance check** (the MIT-weights claim). A re-benchmark
  on real *on-site* captures (angled/night/dirty) is wanted once cameras are installed. See
  [[vision-service]]; AGPL still permitted in-service for the stronger fallback.
- **Vehicle fingerprint**: attribute classifier vs. embedding-similarity; what threshold makes a
  mismatch an anomaly without false-positiving on lighting/angle.
- **Compute footprint** on the appliance (CPU-only vs. a small GPU/NPU) — procurement input
  ([[bom]], [[open-questions]]).
- Per-camera **opt-in** ("optionally bound", user's word): which lanes/cameras route snapshots to
  the service.