Prepare scanner branch for merge

docs/PROJECT.md

@@ -5,6 +5,7 @@ This document is the source of truth for project intent, scope, runtime facts, a
 For implementation structure, see [ARCHITECTURE.md](ARCHITECTURE.md). For engineering standards, see [CONVENTIONS.md](CONVENTIONS.md).
 For the latest Inventory-Kamera comparison work, see
 [scanner-ik-progress-report.md](scanner-ik-progress-report.md).
+For the current branch merge checklist, see [MERGE_READINESS.md](MERGE_READINESS.md).
 
 ## Project Identity
 
@@ -60,7 +61,7 @@ The app is not intended to replace deep min-max tools. It prioritizes time savin
 | --- | --- | --- |
 | Safety | Never perform irreversible in-game actions. | Code review and manual test |
 | Performance | Single artifact read should feel interactive and batch scan should not stall on false progress. | Capture latency monitored manually; auto-scan stops on blocked verification |
-| IK target | First 100 artifacts should scan with accuracy at least as good as Inventory Kamera and equal or better speed. | `npm run scan:goal:compare` quality-gated report |
+| IK target | First 100 artifacts should scan with accuracy at least as good as Inventory Kamera and equal or better speed. | `npm run scan:goal:compare:validated` or `npm run scan:goal:compare:validated:wait` quality-gated report |
 | Privacy | Captures and parsed data stay local by default. | No remote upload in scanner path |
 | Reliability | Uncertain OCR must be visible to the user. | Confidence and details view |
 | Learning loop | Scanner mistakes should become reusable local review samples. | `review-samples.jsonl` |
@@ -104,19 +105,34 @@ The app is not intended to replace deep min-max tools. It prioritizes time savin
 - Elevated live automation is validated in the current dev environment:
   `/automation/probe-click?index=1` changed the selected artifact and
   `/scanner/start?limit=2` completed with 2/2 verified reads and 0 misses.
+- A 2026-07-08 visible-inventory 50-artifact run completed cleanly with
+  50/50 parsed and stored, 0 review, 0 duplicates, and 0 misses. It is stable
+  but still too slow for the 2-3 artifacts/second target.
+- Later 2026-07-08 direct-GDI hot-path runs completed 20/20 parsed with
+  0 misses and 0 review. The best clean 20-artifact iteration reached
+  7285 ms, or roughly 2.75 artifacts/second; the final stable
+  `2026-07-08-direct-gdi-reviewfix` run completed in 7973 ms. The
+  3 artifacts/second target remains unproven.
+- The same direct-GDI path completed a 100-artifact run with 100/100 parsed,
+  0 review, 0 misses, and 42064 ms elapsed across 4 pages.
+- Review samples can now be exported with `npm run eval:review-candidates` into
+  a Git-ignored human-labeling worklist. This is the next quality phase before
+  adding more OCR corpus cases or trusting review queue data as labels.
 
 ### What is still structurally weak
 
 - The scan experience is still partly orchestrated from `src/App.tsx`, which makes behavior changes harder than they should be.
-- Broader scan soak testing still needs to increase the live limit gradually and
-  validate scroll/page transitions beyond the first visible row.
+- Broader scan soak testing has reached clean 20-, 45-, and 100-artifact runs
+  with 0 misses on the current engine. The current-vs-IK-traineddata comparison
+  is now captured; `current` won the qualified 100-artifact comparison on
+  2026-07-08.
 - OCR quality is still inconsistent enough that some fields are recovered by fallback and derivation more often than they should be.
 - Learned fixes currently focus on text replacements; they do not yet update crop offsets, UI profile variants, or scanner targeting rules in a structured way.
 - The scan page is cleaner than before, but it still exposes too much operator/debug state in the main flow.
 - Recommendations and build logic exist, but the scanner is not yet reliable enough to make them the core focus.
-- The latest source has not yet completed the final 100-artifact live comparison
-  because the current dev-control port is still owned by a stale elevated
-  Electron process. Live timing must wait for a UAC-approved restart.
+- The latest source has completed the final current-vs-IK-traineddata live
+  comparison for this environment. Repeatability and 3 artifacts/second are
+  still open.
 
 ### Current product conclusion
 
@@ -171,6 +187,9 @@ Outcome:
   5. safe derivation from piece/slot/value references
 - Main stat/value inference is tightened with slot constraints and reference tables.
 - Bad parses automatically generate structured review reasons.
+- Equipped-character parsing is canonical-data constrained: noisy known names can
+  match through aliases/fuzzy lookup, but unknown footer fragments stay
+  `Not detected` instead of being stored as invented character names.
 
 Status:
 - In progress
@@ -191,6 +210,8 @@ Outcome:
   - resume or stop
 - Progress counts only when a new verified artifact or duplicate signature is confirmed.
 - Repeated pages, unchanged detail cards, blocked cursor movement, and scroll failures stop the scan with diagnosis instead of producing fake progress.
+- Fast artifact-read captures include the equipped footer when an equipped
+  marker is visible; preflight and polling captures still skip expensive OCR.
 
 Status:
 - In progress
@@ -225,7 +246,9 @@ Outcome:
 - Review samples become both parser regression fixtures and learning inputs.
 
 Status:
-- Planned
+- Prepared in code for text replacements, field aliases, constrained fixes,
+  crop adjustment proposals, and UI-profile adjustment proposals. Crop/profile
+  changes still require live review before being auto-applied.
 
 ### Phase 6 - Recommendations come back on top of a trusted scanner
 
@@ -241,11 +264,12 @@ Status:
 1. Finish scan-page cleanup so the main operator view is no longer noisy.
 2. Tighten the game data generator and parser contract, then backfill regression tests from real bad samples.
 3. Continue moving auto-scan behavior out of `App.tsx` and into isolated scanner modules.
-4. Soak-test the elevated C# helper automation path with gradually larger scan limits and page scroll transitions.
-5. Run `npm run scan:goal:compare` after `/health.appBuild.signature` matches
-   the current source and use the quality-gated 100-artifact report as the IK
-   target evidence.
-6. Extend the learning system from text-only fixes into crop/UI profile tuning.
+4. Repeat the qualified current-vs-IK-traineddata comparison in a later live
+   session before making stronger speed/default-engine claims.
+5. Validate equipped-character footer reads and a positive `locked=true` sample
+   from known artifacts; export candidates with `npm run eval:review-candidates`.
+6. Grow the confirmed OCR corpus from review samples before tightening parser
+   thresholds further.
 7. Resume recommendation work only when scan accuracy is consistently trustworthy.
 
 ## Open Questions