AzuTear
639b0b7f59
Add native IK-style capture processing, Artifact Inventory, explicit promotion and single-result review. Confirm the three live OCR corrections in the eval corpus and preserve extraction/value separation.
101 lines
4.6 KiB
Markdown
101 lines
4.6 KiB
Markdown
# OCR Eval Harness
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Field-level accuracy measurement for the artifact OCR parser. This is the gate
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every OCR, crop, layout, or parser change runs against (see ADR-007).
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It is necessary but not sufficient for live scanner proof: scan speed and
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review/miss rates are measured by `npm run scan:iterate:validated` for short
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iteration and `npm run scan:goal:validated` for the final 100-artifact proof.
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Use the `:wait` variants directly after UAC startup.
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## Run it
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```powershell
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npm run eval # full accuracy report for the seed corpus
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npm run eval:review-candidates # export unconfirmed review samples for human labeling
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npm test # runs the eval gate alongside the rest of the suite
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npm run scan:assessment:test # verifies quality-first scan ranking logic
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npm run scan:iterate:validated:wait # 20-artifact live scan
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npm run scan:repeatability:wait # 20/45/100 current-engine repeatability
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npm run scan:goal:validated:wait # final 100-artifact live scan
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```
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The report prints exact-match rate, overall field accuracy, a per-field
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breakdown (critical fields marked with `*`), and every failing case with an
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`expected "..." got "..."` diff.
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Repeatability runs prove whether the visible-inventory scanner stays stable
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across later sessions. They are current-path evidence and should be reported
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with review/miss rates plus timing, not just click count.
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## How it works
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- `src/eval/ocrEvalHarness.ts` - pure metric functions. `runOcrEval(cases)`
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feeds each case's OCR text through the real `parseArtifactCandidate` and scores
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the produced fields against the labels. Order-independent for substats.
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- `src/eval/corpus/seedCorpus.ts` - the seed corpus, transcribed from the
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verified assertions in `src/lib/artifactOcrParser.test.ts`. Must stay at 100%.
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- `src/eval/ocrEval.test.ts` - the gate: seed field accuracy, critical-field
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accuracy, and exact-match rate must all be 1.0.
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## Growing the corpus from review samples
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The review queue is the corpus source. A saved review sample carries the OCR
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text plus the parser's *guess* - `reviewSampleToEvalCase` extracts both.
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For the local Electron queue, run:
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```powershell
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npm run eval:review-candidates -- --limit=80
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```
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This writes:
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- `outputs/review-eval-candidates/review-eval-candidates.json`
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- `outputs/review-eval-candidates/review-eval-candidates.md`
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The exporter deduplicates samples, puts complete modern OCR captures first,
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marks missing fast-profile fields so stale/partial captures do not crowd out
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useful cases, and surfaces ownership/lock evidence (`artifact-footer`,
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`equipped`, and `locked=true/false`) for the next validation pass.
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After manually checking one candidate against the real artifact, create a
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confirmed corpus snippet with explicit expected labels:
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```powershell
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npm run eval:prepare-confirmed -- --candidate=<candidate-id> --expect-file=.\path\to\expect.json
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```
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The script reads the latest
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`outputs/review-eval-candidates/review-eval-candidates.json` by default and
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writes a `.confirmed.ts` snippet under `outputs/review-eval-candidates/`.
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It refuses to run without explicit labels, so parser guesses are not silently
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promoted to ground truth. Review that snippet, then paste the object into
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`src/eval/corpus/confirmedReviewCorpus.ts`.
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The parser's guess is a label **candidate, not ground truth** (using it directly
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would be the parser grading itself). To add a real case:
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1. Convert review samples with `reviewSamplesToEvalCases(records)`.
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2. Open each produced case and confirm or correct the `expect` values against
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what the artifact actually is in-game. Set `confirmed: true`.
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3. Move the corrected case into
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`src/eval/corpus/confirmedReviewCorpus.ts`. The main eval gate imports
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`src/eval/corpus/index.ts`, which combines the seed corpus with confirmed
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review cases.
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`src/eval/corpus/confirmedReviewCorpus.test.ts` rejects common corpus mistakes:
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duplicate case ids, missing OCR text, empty labels, missing source notes, or a
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case that was copied in without `confirmed: true`.
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Prefer cases that cover new failure modes: unseen resolutions, new sets or
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characters, equipped footer noise, and OCR noise the current corpus does not
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exercise. `locked` is a capture-side visual flag rather than a text parser field;
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validate it from review-export metadata and live screenshots instead of adding
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it to the OCR eval labels.
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## When a change moves a number
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- Accuracy **drops**: a regression. Read the printed failures; fix the parser or
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revert. Do not lower the threshold to make it pass.
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- A change **intentionally** alters a previously-correct output: update the
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corpus label in the same commit. The label is the source of truth, not the code.
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