By George Curta · Last updated 2026-08-12
Redact PII Inside Images
Legal exhibits, scanned ID documents, and HR screenshots often carry personal data as pixels, not text. Our image pipeline runs OCR, detects PII in the extracted text, and covers each match with a black box.
How It Works
Three steps, run server-side on your uploaded image.
1. OCR
Tesseract extracts the text layer from your PNG or JPG, including its position on the page.
2. Detect
The extracted text runs through the same PII detector used for documents, locating names, ID numbers, dates, and other entities with a confidence score.
3. Redact
Each detected region is covered with a solid black box (the default fill) directly on the image, and the redacted file is returned for download.
Where This Helps
Any scanned or photographed document where PII sits in the image itself.
Legal Exhibits
Redact names, addresses, and case numbers on scanned exhibits and evidence photos before production or filing.
ID Documents
Cover passport, driver's license, and national ID numbers on photographed or scanned identity documents.
HR Scans
Redact personal data on scanned employee forms, timesheets, and onboarding paperwork before sharing or archiving.
Supported Formats
Two image formats are supported today.
Supported
- PNG
- JPG / JPEG
Not Yet Supported
- • PDF page images (use the PDF pipeline for text-layer PDFs instead)
- • Other raster formats (e.g. TIFF, BMP, WebP, GIF)
Basic Plan and Up
Image redaction requires a Basic plan or higher. Free-plan accounts can anonymize text but not images; upgrading unlocks PNG/JPG uploads at your plan's file-size limit.
View PlansFrequently Asked Questions
Which image formats does image redaction support?
PNG and JPG (JPEG). Other raster formats such as TIFF, BMP, WebP, and GIF are not currently supported.
Does image redaction require a paid plan?
Yes. Image redaction requires a Basic plan or higher. The free plan supports text anonymization only.
How does it find PII inside an image?
The image is run through OCR (Tesseract) to extract its text layer and each piece of text's position, then that text is analyzed by the same PII detector used for documents. Detected regions are covered with a black box on the image itself.
Is the redaction reversible?
No. Image redaction covers the detected region with an opaque black box directly on the image pixels — this is a one-way, visual redaction, not a reversible encryption like the text anonymizer offers.
How accurate is OCR-based detection on scans?
Accuracy depends on scan quality, resolution, and handwriting versus print. Low-quality scans and handwritten text lower OCR accuracy, which lowers detection recall — review redacted output before relying on it for high-stakes documents.
Limitations / Important note
- OCR-based text extraction is probabilistic; low-quality scans and handwriting lower detection recall.
- Redaction is a visual, one-way black box over the detected region — not reversible encryption.
- This page is informational and is not legal advice.
About this page
We update this page when our platform or the law changes.
Read our founder note for how we work.
Each change shows up in the timestamp at the top.
Related reading
We follow these rules
- GDPR (EU 2016/679).
- ISO/IEC 27001:2022, held by our hosting provider.
- NIS2 (EU 2022/2555).
- HIPAA safe harbor under 45 CFR § 164.514(b)(2).
Our promise
We do not sell your data.
We do not train models on your text.
We store your files in Germany.
You can delete your account at any time.
You own your work.
Where we run
Our company HQ is in Saarbrücken, Germany. Our servers run in Hetzner's Falkenstein datacenter.
Hetzner holds ISO 27001 certification.
All data stays in the EU.
Backups run every day.
Need help?
Email support@anonym.legal.
We reply within one business day.
How we test
Automated checks run on every release.
Each surface gets its own sweep script and report.
Human reviewers spot-check the output each week.
We test detection against sample documents before each release.
A failing unit or integration run stops the release.
What we never do
- We never sell your information to third parties.
- We never train models on what you upload.
- We never keep your work after you delete it.
- We never share keys with any outside firm.
- We never run ads inside the product.
Plans in plain words
We sell credits, not seats.
One credit covers one short job.
Long jobs use a few credits each.
Paid plans can buy top-up credits.
Credits reset at the end of each cycle.
Read the plans page for current rates.
Who built this
A small team of engineers and lawyers built this.
We ship from Europe and work in the open.
Our founder note spells out why we started.
Where to start
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
How the parts fit
A browser add-on cleans text inside Chrome.
A Word plug-in handles drafts in Office.
A small desktop tool works on whole folders.
An agent protocol link feeds large models safely.
All four share one core engine and one rule set.
Words from our team
We started this work after a lunch about cookies.
One friend kept getting odd ads on her phone.
We asked why a court file leaked through a draft.
We sketched the first build on a napkin that week.
By month three we had a tiny demo for a friend.
She used it on her first case the next day.
Common questions we hear
Can the tool read scanned PDFs? Yes, with OCR.
Does it work on long files? Yes, in small chunks.
Can I roll my own rule set? Yes, save it as a preset.
Does it run offline? The desktop build runs offline.
Do you keep my files? No, the cloud build wipes after each run.
Will it learn from my work? No, we never train on inputs.
A short tour of the workflow
Upload a file or paste a snippet of prose.
Pick the entities you want gone from the draft.
Choose a method: replace, mask, hash, encrypt, or redact.
Press run and watch the side panel show each hit.
Skim the result and tweak any rule that misfired.
Save the cleaned file or send it to a teammate.