Normalize Unicode Text
Normalize Unicode to NFC form in one click — perfect for fixing string comparison bugs and hidden codepoints.
How to Use This Normalize Unicode Text
Step 1
Paste your messy multilingual text
Step 2
See the NFC-normalized version
Step 3
Compare character counts to spot decomposed input
Step 4
Hit Copy and use the canonical form
What Is Normalize Unicode Text?
Two strings look identical but fail equality checks because one uses composed characters and the other decomposed. Hidden zero-width joiners hide inside pasted PDF text.
Paste any messy Unicode and get a clean NFC-normalized version back.
If you're debugging form input, you fix 'café != café' bugs. If you're indexing for search, you keep matches consistent. If you're prepping translation memory, you ensure CAT tools find segments reliably.
Frequently Asked Questions
NFC vs NFD?
NFC composes accent+letter into one codepoint. NFD splits them into combining marks.
Tip: NFC is the web/database default. NFD is for macOS filesystems.
Will it remove zero-width characters?
It normalizes them but doesn't delete them. Use Remove Special Characters for that.
Tip: Don't strip ZWJ from emoji families.
Why do equal-looking strings fail comparison?
Different Unicode forms. Normalize both to NFC before comparing.
Tip: This is the #1 cause of 'café' bugs in JS and Python.
Does it convert full-width CJK?
NFC doesn't, NFKC does. This tool defaults to NFC.
Tip: Use NFKC mode to convert ０１２ to 012.
Will emojis still render?
Properly composed ZWJ sequences are preserved.
Tip: Family and profession emojis stay intact.
Should I always normalize input?
Yes, normalize once on submission so downstream comparisons match.
Tip: NFC for storage, NFD only for macOS filenames.