---

name: image-redaction description: "Operate Presidio image OCR redaction for standard images and DICOM pixel PHI." disable-model-invocation: true metadata: disco-role: operating license: MIT

image-redaction

Use this sub-skill when the task is about Presidio Image Redactor package workflows: OCR text detection in images, redacting image pixels, returning or validating bounding boxes, choosing the OCR engine, or handling DICOM burnt-in text redaction.

Use when

• You need ImageRedactorEngine.redact() or redact_and_return_bbox() on a PIL image, image-like object, NumPy array, or image file path accepted by the image preprocessor.
• You need ImageAnalyzerEngine.analyze() to run OCR, feed extracted text through Presidio Analyzer, and map analyzer spans back to image bounding boxes.
• You need ImagePiiVerifyEngine.verify() for an annotated verification image showing OCR/PII boxes.
• You need DicomImageRedactorEngine for DICOM pixel redaction from a loaded pydicom instance, one file, or a directory tree.
• You need DicomImagePiiVerifyEngine to create DICOM verification overlays or compute precision/recall against ground-truth bounding boxes.
• You need to choose or troubleshoot TesseractOCR versus optional DocumentIntelligenceOCR.
• You need ImageRecognizerResult, bbox coordinate semantics, color/fill choices, ocr_kwargs, or analyzer kwargs passed through image workflows.

Route elsewhere

• Text analyzer tuning, recognizer design, entity/language lists, score thresholds, context, allow lists, NLP engine configuration, or missing spaCy model diagnosis beyond the image pass-through: ../analyze-text/SKILL.md.
• Text anonymization operators or deanonymization: ../anonymize-text/SKILL.md.
• HTTP service startup, Docker/container guidance, or REST endpoint use: ../../references/service-and-rest-api.md.
• DICOM metadata de-identification: outside this sub-skill. This sub-skill only covers pixel redaction; metadata PHI must be scrubbed by a DICOM metadata de-identification workflow after pixel redaction.

Read first

• references/api-reference.md for constructors, methods, return values, bbox shape, fill values, and kwarg routing.
• references/ocr-and-dicom-workflows.md for standard image, custom analyzer, Azure Document Intelligence, DICOM file/directory, and verification recipes.
• references/troubleshooting.md for missing Tesseract, empty OCR, analyzer/model failures, Azure credentials, pydicom/GDCM, and bbox/fill surprises.
• scripts/image_ocr_smoke.py for a no-source-path smoke check that imports the package, checks Tesseract, creates an in-memory image, and verifies at least one redaction bbox.

Operating checklist

1. Confirm prerequisites: presidio-image-redactor is importable; Tesseract is on PATH for the default TesseractOCR; the default analyzer path has en_core_web_lg unless you inject a custom analyzer that does not need it.
2. Decide OCR backend:
   • Use TesseractOCR by default for local image and DICOM workflows.
   • Use DocumentIntelligenceOCR only when Azure Document Intelligence credentials and one-page document constraints are acceptable.
3. Decide analyzer behavior:
   • Pass image workflow kwargs such as language, entities, score_threshold, allow_list, allow_list_match, and context through to the underlying AnalyzerEngine.analyze().
   • Route recognizer/NLP design details to ../analyze-text/SKILL.md.
4. For standard images, choose ImageRedactorEngine.redact() when only the image is needed; choose redact_and_return_bbox() when you need ImageRecognizerResult objects for validation or downstream review.
5. For DICOM, choose redact_and_return_bbox() for loaded instances and redact_from_file()/redact_from_directory() for filesystem workflows. Use save_bboxes=True when you need sidecar bbox JSON.
6. Treat use_metadata=True as a pixel-detection aid only: it uses DICOM metadata terms to find matching burnt-in pixel text, but it does not remove or alter metadata PHI.
7. When a run returns no boxes, debug in this order: Tesseract availability, raw OCR words, image resolution/contrast/orientation, ocr_threshold, analyzer kwargs/entities/language/model, then bbox mapping.

Fast facts

• Standard-image fill is an integer grayscale value or an RGB tuple, for example 1, 255, or (255, 0, 0).
• DICOM fill is "contrast"/"invert"/"inverted"/"inverse" for a contrasting mask, or "background"/"bg" for a background-like mask.
• ocr_kwargs is forwarded to the OCR backend after image code strips ocr_threshold; ocr_threshold accepts values from -1 through 100.
• Analyzer kwargs are passed through to Presidio Analyzer; keep complex analyzer construction in ../analyze-text/SKILL.md and inject the resulting AnalyzerEngine into ImageAnalyzerEngine.
• ImageRecognizerResult has text span fields plus pixel fields: entity_type, start, end, score, left, top, width, height.
• Multi-word or overlapping analyzer results can produce multiple boxes for one entity; overlapping image boxes can represent different entity labels for the same OCR word.
• DICOM file/directory helpers duplicate inputs into the requested output directory before editing; loaded-instance methods return a copied in-memory dataset.