| name | read-text |
| description | Extract structured text, numbers, labels, and codes from images and evaluate by character or field accuracy. TRIGGER when: user asks to read/scan a serial, part/date/lot number, label, license plate, meter, gauge, barcode, QR code, invoice, document, form field, or other image text. SKIP when: user asks to tally/count boxes or other objects on a conveyor (detect-and-analyze), pass/fail without text (classify-or-flag), measure crack width/masks/area (segment-and-analyze), track objects (track-and-count), cost only (estimate-economics), or integrate/deliver working OCR (deliver-cv-project).
|
| allowed-tools | Bash, Read, Write, Edit, Glob, Grep, AskUserQuestion |
Extract the exact fields needed by the business process and clear an independently transcribed acceptance set. Separate region localization, text decoding, normalization, and field validation so failures have an owner.
Platform execution boundary. Read ../../resources/roboflow-platform-lookup.md before any provider-specific capability, dataset, training, inference, workflow, or deployment action. Delegate exact execution to the installed official Roboflow skill or current MCP resource. Do not preserve current engine names, blocks, IDs, or invocation syntax here.
Follow ../../resources/fde-methodology.md; apply these text-specific rules.
1. Define fields and operational action
Inspect representative images, existing schemas, expected formats, code, and error handling. Ask at most three missing questions:
- Which exact fields must be returned, and what action uses them?
- Are substitutions allowed, or must the whole field match exactly?
- Which capture conditions and scripts/languages occur in production?
Freeze before selecting or tuning a candidate:
Acceptance ID: <session/revision>
Business decision: <action enabled by extracted fields>
Gold set: <independent transcription source, split, adjudicator>
Primary metric and threshold: <exact field match/CER/valid-code rate>
Secondary guardrails: <critical-field recall, rejection rate, latency>
Frozen before baseline: <timestamp and confirmation>
Baseline result (diagnostic only): <not run yet>
2. Freeze the extraction schema
For each field, record name, type, allowed alphabet, normalization, format/checksum rule, null behavior, confidence/review policy, and whether localization is needed. Preserve raw text separately from normalized output. Never silently “correct” a value unless the correction rule was frozen and both forms are retained.
3. Select and evaluate candidates
Ask upstream for current text/code-reading capabilities with provenance, license, input constraints, and runtime requirements. Prefer a no-training capability that clears the same independent gate. Do not encode provider model IDs or command sequences in Sentinel.
Measure the full localize → decode → normalize → validate pipeline. Report:
- exact field-match rate with numerator/denominator;
- character error rate where partial correctness matters;
- critical-field recall and false acceptance/rejection rate;
- invalid-format/checksum and abstention counts;
- slices by blur, glare, font, layout, language, crop, and camera;
- p50/p95 latency on the target runtime.
Manual visual review helps diagnose samples but cannot be converted into an accuracy claim without independent transcription.
4. Diagnose a failed gate
Separate missing/wrong regions, decoding substitutions/deletions, orientation/quality issues, normalization bugs, and validator errors. Test capture, crop/orientation, deterministic validation, or candidate changes before custom training. Target new labels at demonstrated failure slices. Delegate platform actions upstream and remeasure on the frozen set.
5. Decide and deliver
Return go, revise, or stop, including field-level errors, rejection policy, privacy boundary, and remaining manual-review cases. Follow ../../resources/artifact-contract.md; emit a verified hosted-client, offline-tested local-runtime, or candid scaffold. A passing extraction capability routes to deliver-cv-project.
- Treat extracted personal/document data as sensitive and minimize retention.
- Never invent missing characters or suppress the raw result.
- Pseudo-transcriptions cannot own acceptance.
- Obtain explicit consent before data movement or paid work.
- Delegate exact provider operations to current upstream guidance.
Follow ../../resources/ledger-protocol.md. Record acceptance, field-level evaluation, artifact verification, and decision once with non-empty event IDs and status.
<stop_rules>
- Field schema/normalization is undefined → freeze it first.
- No independent transcription set → do not claim accuracy.
- Capture quality makes characters unresolvable → state the acquisition requirement.
- Paid/data-moving action lacks current-turn consent → stop.
- Applicable live/offline smoke is absent → retain
scaffold status.
</stop_rules>
