Find invisible Unicode characters in text
Reveal zero-width spaces, non-breaking spaces, smart quotes, and look-alike Unicode characters that break code, config, or searches.
Open the tool, then paste the sample input below. Everything runs locally in your browser.
The problem
Invisible and look-alike characters can make two strings appear identical while code, queries, or config comparisons fail. Inspecting code points shows what is actually in the text.
Sample input
user@example.comExpected output
Found U+200B ZERO WIDTH SPACE after .com
Suggested cleanup: remove invisible characterHow to do it
- Paste the suspicious text.
- Run the inspection.
- Review code points and invisible character warnings.
- Copy the cleaned text if needed.
- Re-test the value in the target system.
Common mistakes
- Trusting what text looks like on screen.
- Missing non-breaking spaces copied from documents.
- Confusing hyphen, en dash, and minus sign.
- Leaving zero-width characters in identifiers or URLs.
Related tools
FAQ
What is a zero-width character?
It is a Unicode character that affects text but has no visible width, such as U+200B zero-width space.
Why do smart quotes break config files?
Many formats require plain ASCII quotes. Smart quotes are different Unicode characters.
Is my text uploaded?
No. Unicode inspection runs locally in your browser.
Is my data uploaded anywhere?
No. This workflow runs locally in your browser unless you explicitly copy or share the result yourself.
This guide uses browser-local tooling. Avoid pasting production secrets unless you understand what the tool displays and shares.
Continue with adjacent browser-based tools for the same workflow.
Find invisible text characters: quick answer
Use this when a parser, login form, diff, slug, or test assertion fails even though the text looks correct. Paste or load the artifact into the linked tool, run the local check, then copy only the safe result or summary into your PR, ticket, or test notes.
What to verify
Check zero-width spaces, BOMs, non-breaking spaces, smart quotes, control characters, mixed normalization, and copied text from documents. If a result will be shared outside your team, run a privacy or secret scan first and replace real values with safe examples.
Recommended next steps
- Open Unicode Inspector for the main task.
- Use Text Diff when you need a second validation pass.
- Return to Use Cases to find related workflows for the same artifact.
Practical example and expected result
Reveal zero-width spaces, non-breaking spaces, smart quotes, and look-alike Unicode characters that break code, config, or searches. In practice, this is most useful when you need a quick, repeatable check on a HAR capture before adding it to a ticket, pull request, test fixture, or support note.
A realistic input for this workflow is user@example.com. The expected result should resemble Found U+200B ZERO WIDTH SPACE after .com Suggested cleanup: remove invisible character, with the same important values preserved.
Troubleshooting checklist
- Confirm you copied the complete HAR capture and not only a partial line or truncated preview.
- Run the local tool once with a safe sample, then repeat with the real data only if your team policy allows it.
- Check quoting, escaping, whitespace, encoding, timestamps, and environment-specific values before trusting the result.
- Before sharing output, remove secrets, tokens, cookies, customer data, and production hostnames that are not needed for the review.
Next useful steps
- Open this example in Unicode & Invisible Character Inspector → for a related validation or follow-up step.
- String Escape / Unescape for a related validation or follow-up step.
- URL Parser for a related validation or follow-up step.
- Use Magic Box when you are not sure which tool should handle the next artifact.
Review checklist before sharing the result
For Find invisible Unicode characters in text, the safest workflow is to test with a small sample, confirm the output shape, then repeat with the real artifact only when needed. Keep the original input open until you verify that no value was dropped, decoded twice, sorted unexpectedly, or changed from text into a different type.
Before copying the result into a pull request, issue, chat thread, or support ticket, scan for private values. Replace real IDs, bearer tokens, session cookies, email addresses, internal hostnames, and customer data with placeholders. If another tool is listed above, use it as the second pass rather than manually editing complex output.
- Keep one line of context explaining why the transformation was needed.
- Preserve enough sample structure for a reviewer to reproduce the result locally.
- Note any assumptions about encoding, timezone, delimiter, schema version, or runtime environment.