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.

返回功能

涂黑图像中的 PII

法律证物、扫描的身份证件和 HR 截图，其中的个人数据常常以像素而非文字的形式存在。我们的图像处理流程会运行 OCR，在提取出的文字中检测 PII，并用黑框遮盖每一处匹配。

在网页应用中试用 API 参考桌面应用

工作原理

三个步骤，在服务器端对您上传的图像执行。

1. OCR

Tesseract 从您的 PNG 或 JPG 图像中提取文字层，包括每段文字在页面上的位置。

2. 检测

提取出的文字会经过与文档处理相同的 PII 检测器，定位姓名、证件号码、日期等实体，并给出置信度分数。

3. 涂黑

每个检测到的区域都会直接在图像上用实心黑框（默认填充色）覆盖，涂黑后的文件将提供下载。

适用场景

任何 PII 本身就存在于图像中的扫描件或拍摄文档。

法律证物

在提交或归档前，涂黑扫描证物和证据照片上的姓名、地址和案件编号。

身份证件

遮盖拍摄或扫描的身份证件上的护照、驾照和国民身份证号码。

HR 扫描件

在分享或归档前，涂黑扫描的员工表格、考勤表和入职材料上的个人数据。

支持的格式

目前支持两种图像格式。

支持

PNG
JPG / JPEG

暂不支持

• PDF 页面图像（若为文字层 PDF，请改用 PDF 处理流程）
• 其他光栅格式（例如 TIFF、BMP、WebP、GIF）

Basic 及以上计划

图像涂黑功能需要 Basic 或更高级别的计划。免费计划账户可以匿名化文本，但无法处理图像；升级后即可在您计划的文件大小限制内上传 PNG/JPG。

查看计划

常见问题

图像涂黑支持哪些图像格式？

PNG 和 JPG（JPEG）。目前不支持 TIFF、BMP、WebP、GIF 等其他光栅格式。

图像涂黑是否需要付费计划？

是的。图像涂黑需要 Basic 或更高级别的计划。免费计划仅支持文本匿名化。

它是如何在图像中找到 PII 的？

图像会先经过 OCR（Tesseract）处理，提取出文字层及每段文字的位置，然后由与文档处理相同的 PII 检测器对文字进行分析。检测到的区域会直接在图像上用黑框覆盖。

涂黑是否可逆？

不可逆。图像涂黑是直接在图像像素上用不透明黑框覆盖检测到的区域——这是一种单向的视觉涂黑，而非文本匿名化器提供的那种可逆加密。

基于 OCR 的检测在扫描件上的准确率如何？

准确率取决于扫描质量、分辨率，以及手写还是印刷文字。低质量扫描件和手写文字会降低 OCR 的准确率，进而降低检测召回率——在将涂黑结果用于高风险文档之前，请务必先行审核。

涂黑您图像中的 PII

在网页应用中上传 PNG 或 JPG，或直接调用 API。

免费试用

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.

How we measure — our methodology

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.

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

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.