AzuTear
53 lines
2.3 KiB
Markdown
53 lines
2.3 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 the IK target: live scan speed and
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review/miss rates are measured by `npm run scan:goal:compare`.
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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 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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```
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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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## 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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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 a file under `src/eval/corpus/` and add it to the
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corpus array.
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Prefer cases that cover new failure modes: unseen resolutions, new sets or
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characters, and OCR noise the current corpus does not exercise.
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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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