AI Text Watermark Remover.

AI text watermarks are invisible characters inserted into the Unicode stream of generated text. Unlike image watermarks that modify pixel data or file headers, text watermarks exploit the Unicode standard's large inventory of non-printing characters — code points that are defined, valid, and processable, but that render as nothing visible.

The primary watermarking vectors in AI text:

Zero-width characters: U+200B (Zero Width Space), U+200C (Zero Width Non-Joiner), U+200D (Zero Width Joiner), U+2060 (Word Joiner), and related code points. These can be inserted between any characters in a string without affecting visual rendering.

Bidirectional control characters: U+202A through U+202E control the direction of text rendering. Inserting these in strategic positions creates invisible formatting markers that persist through most text processing.

Homoglyph substitution: Replacing standard ASCII characters with visually identical characters from other Unicode blocks. For example, replacing a Latin 'a' with a Cyrillic 'а' — visually identical but a different code point. This approach is more sophisticated and less commonly documented in standard AI output but is used in some watermarking research.

Soft hyphen (U+00AD): Invisible in most contexts but present in the character stream.

All of these are detectable by systems that scan at the Unicode code point level, including AI detection platforms and some plagiarism detection tools.

The extent and specifics of text watermarking vary by model and have not been fully disclosed by any major AI lab. What researchers and users have documented through character-level analysis:

ChatGPT outputs have shown zero-width characters in text generated by GPT-4 and GPT-4o. The characters appear non-randomly distributed, suggesting systematic insertion rather than generation artifacts.

Gemini text outputs have also shown zero-width character presence in text generated through Gemini's chat interface and API.

Claude outputs have been analyzed with more mixed results — zero-width characters appear less consistently in Claude outputs but are not absent.

Google has published research on text watermarking for its language models using a different technique: token-level probability watermarking. Instead of inserting invisible characters, this approach biases the token sampling process to embed a statistically detectable pattern in word choice distributions. This type of watermark cannot be removed by stripping Unicode characters — it is embedded in the statistical pattern of the text itself and requires rewriting to address.

This tool addresses the Unicode character-level watermarks. Statistical watermarks require humanization tools that rewrite the content.

1. Generate your content with any AI model — ChatGPT, Claude, Gemini, Grok, or any other.

1. Copy the generated text directly from the interface. The copy operation preserves zero-width characters — they come along with the visible text.

1. Paste into the input area of this tool.

1. Click Clean Text. The scanner checks every character against the full list of known invisible and zero-width Unicode code points.

1. The removed count shows how many invisible characters were found. If zero are found, the text was clean or the model did not embed characters in this output.

1. Copy the cleaned output. The text is identical in visual appearance but free of invisible watermark characters.

Processing is instant and runs locally in your browser. No text is transmitted to any server.

These are two distinct operations addressing two distinct detection mechanisms. Understanding the difference matters for choosing the right approach.

Text watermark removal strips invisible Unicode characters from the text string. It addresses the technical, character-level detection signal. The visible text is completely unchanged. This operation is free and runs in seconds.

AI humanization rewrites the text itself — changing word choices, sentence structures, phrasing patterns, and stylistic elements that are statistically associated with AI-generated writing. It addresses the pattern-level detection signals used by detectors like GPTZero, Turnitin, and Originality.ai. This is a more involved transformation and typically requires more sophisticated processing.

For comprehensive AI detection evasion, both layers matter. The character-level clean removes the explicit invisible markers. The humanization addresses the statistical patterns. Running this text watermark remover first, then using a humanization tool, covers both vectors. Start here — it is free and instant — then decide whether the statistical rewriting step is also needed for your use case.

All processing in this tool runs entirely in your browser. Your text never leaves your device. The JavaScript scanning function runs locally, comparing each character against the list of invisible Unicode code points and filtering them out of the string.

No copy of your original text, the cleaned text, or any logs are sent to or stored on any server. There are no analytics on the content of what you paste. The tool has no awareness of what you submitted — it only knows a string was processed and how many characters were removed.

This local-first approach is particularly important for professional and academic content where the text itself may be confidential, proprietary, or under embargo. You can clean sensitive content with no exposure risk.