deckhearth/.convoys/improve-scan-card-detection.md
varutasu 0b4f419f49
Scanner identify upgrade — Phase 1 hot path (#156)
* docs(convoy): seed scanner identify upgrade epic and sub-convoys

Baseline scan_attempts telemetry and three-phase plan for faster, more
accurate card identification without touching scanner chrome.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(scanner): tighten Layer-1 identify hot path (Phase 1)

Cut verify hold-still gates, OCR collector numbers on Layer 1, request
structured Gemini JSON, and skip automatic L2 refine when L1 opens the
printing picker. Includes convoy UX/architecture briefs and unit tests.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-14 20:21:20 -05:00

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---
name: improve-scan-card-detection
classification: feature
success_metric: |
After ship, L2 result_kind=not_a_card ≤15% of L2 (was 44.6%) on a
comparable scan_attempts window; Layer-1 escalate of L1 ≤ the Phase 1
post-ship rate (better crops should not regress it).
skip:
- ia
- ui-design
- flag
status: open
created: 2026-08-14
depends_on:
- tighten-scan-identify-hot-path
umbrella: scanner-identify-upgrade
model_policy:
default_session: auto
roles:
role-conductor: composer-2.5-fast
role-architect: composer-2.5
role-ia-architect: composer-2.5-fast
role-ux-reviewer: composer-2.5-fast
role-ui-designer: composer-2.5-fast
role-implementer: composer-2.5-fast
role-reviewer: cursor-grok-4.5-high
role-security-auditor: gpt-5.6-terra-medium
role-design-system-auditor: cursor-grok-4.5-high
role-a11y-auditor: cursor-grok-4.5-high
role-doc-writer: auto
escalate_to: claude-sonnet-5-thinking-medium
escalate_to_premium: claude-4.6-opus-high-thinking
never_premium:
- role-reviewer
- role-security-auditor
- role-design-system-auditor
- role-a11y-auditor
- role-ui-designer
- role-doc-writer
---
# Convoy: improve-scan-card-detection
Phase 2 of `scanner-identify-upgrade`. Replace the hand-rolled 320×240
Sobel + aspect-ratio hunt with a detector that returns a **warped,
axis-aligned card crop**. Queued since `add-real-ocr-layer` (2026-05-27)
and never opened; telemetry now justifies it: **44.6% of Gemini calls
are `not_a_card`**.
`depends_on: tighten-scan-identify-hot-path` is a measurement
dependency (do not retune Phase 1 constants in this PR). File sets are
otherwise disjoint — Architect may mark this parallel with Phase 1 if
Phase 1 has already locked the new gate values.
## Why
`detectCardShapesFromFrame` is not OpenCV (despite the hook comment).
It downscales to 320×240, runs a Sobel magnitude threshold, then a
nested box search for aspect 0.630.77. There is no four-corner
homography. Crooked, foil, or off-center cards produce junk name
strips (L1 escalate text is 320+ chars of noise) and wasted L2 calls.
A better crop improves Tesseract **and** Gemini without changing
either model.
## Scope
### In scope
- Replace or wrap `detectCardShapesFromFrame` so tracked bounds are
card-shaped **and** a perspective-corrected JPEG can be produced for
identify.
- Architect picks **one** in-browser approach (do not land both):
1. **OpenCV.js contours + approxPolyDP + warpPerspective** — no
training, larger WASM.
2. **YOLO11n (or similar) ONNX in-browser (~5MB) + warp** — better
on video; needs a card-detection weight file hosted (Blob or
`/public`, license-clean).
- Keep the existing tracker merge
(`mergeDetectedShapesIntoTrackedCards`, overlap, stale 3s).
- Overlay brackets in `components/scanner/ScannerCamera.js` keep
consuming `{x,y,width,height}` (or four corners mapped to a rect).
Do not redesign the overlay.
- Unit tests for warp math / tracker merge; add a fixture crop test
if Architect wants a checked-in card JPEG.
### Out of scope
- Identify / Gemini / Tesseract / catalog match (Phase 1 + 3).
- Python/CUDA microservice, PaddleOCR server, Roboflow-hosted detect.
- Scanner chrome / cart (`scanner-mobile-checkout`).
- Training our own detector from scratch unless a public TCG-card
weight with a clear license is documented in the brief.
## Roles invoked
1. `role-ux-reviewer` — time-to-bracket, false-positive boxes, dual-card
frames.
2. `role-architect` — OpenCV.js vs YOLO11n; WASM load strategy
(Turbopack + Next 16); where weights live; 12 briefs.
3. `role-implementer`.
4. Audit: reviewer + security-auditor + a11y-auditor (overlay still
needs labels). Design-system only if overlay styling changes.
## Todos
- [ ] Architect: pick detector; document WASM / weight budget
- [ ] Brief 1 — detect + warp library + tests
- [ ] Brief 2 — wire `use-camera-scanner.js` + crop used by
`identifyTrackedCardCapture`
- [ ] Confirm `scanner-mobile-checkout` overlay still maps bounds
- [ ] Re-measure L2 `not_a_card` share
## Likely file ownership
| Area | Files |
| --- | --- |
| Detect | `lib/scanner-card-detection.js`, `test/lib/scanner-card-detection.test.js` |
| Warp helper | new `lib/scanner-card-warp.js` (if Architect splits) |
| Camera loop | `lib/use-camera-scanner.js` |
| Crop consumer | `lib/scanner-card-identify.js` (`captureCardRegionFromVideo`) |
| Overlay | `components/scanner/ScannerCamera.js` only if bounds shape changes |
Do not edit `lib/ocr-worker.js` or `lib/scan-vision.js` here.
## Multitask dispatch
Serial unless Architect splits detect-lib vs camera-wire with disjoint
files.
Audit group id: `audit-improve-scan-card-detection-<pr>`.
## CI impact
| Workflow / job | Behavior |
| --- | --- |
| `visual-diff.yml` | **May fire** if `ScannerCamera.js` changes |
| `preview-smoke.yml` | Fires |
| `ci.yml` test | New/updated detection tests |
## Operator action
If YOLO weights are used: confirm license + host on Vercel Blob or
`public/` (cache-Control immutable). No new secrets.
## Conductor notes
Do not drop OpenCV.js in only to reimplement the current rectangle
hunt. Success is a **rectified card image**, not a prettier box.
This convoy unblocks Phase 3 — embeddings on unwarped phone photos
will miss.