Stripping accents from text means replacing diacritic-marked Latin letters like é, ç, ñ, ø, ł, and ž with their plain ASCII equivalents — café becomes cafe, señor becomes senor, Zürich becomes Zurich, and Łódź becomes Lodz — so the cleaned string is safe for ASCII-only systems, slug generators, deduplication, and identifier matching. The phrase "how to remove accents from keyboard" misleads more than it helps, because the work is almost never about the keyboard layout. The accented characters are already on the clipboard inside a CSV column, a contact list, a product catalog, or a name column that needs to match against a legacy system that does not speak Unicode. The job is text cleanup, not a new typing shortcut, and the cleanup happens once you have the raw text, not while you are typing it. That is the everyday situation Remove Accents from Text is built for: paste in any text, click once, and copy a cleaned string that is actually usable downstream.
The standard quick-script is two lines — normalize to Unicode NFD and delete the combining marks — and on the surface it works. The same approach is built into JavaScript's String.prototype.normalize() method and recommended in the Unicode Normalization Forms specification. But the recipe silently fails on a well-known family of letters that carry no separable accent at all, and it silently corrupts a different family of scripts that decompose exactly the way accented Latin does. Both problems are real for anyone cleaning a contact list, slugging a URL, or matching names across systems that disagree on encoding.
Why Unicode Normalization Alone Leaves Letters Behind
Accented Latin letters such as é, à, ç, and ñ are stored in Unicode as a base letter plus a combining mark. The combining mark is a separate code point — the acute accent U+0301, the grave U+0300, the cedilla U+0327 — so the obvious recipe is to split the pair with NFD normalization and delete the second code point. That pairing is the foundation of the standard approach, and works exactly as the spec says it should.
The problem is that the method only works for letters that have a decomposition entry in the Unicode Character Database. A whole family of Latin letters has no decomposition, because their diacritic is welded into the letter rather than stacked on top of it:
- Slashed and barred letters: ø, Ø, đ, Đ, ħ, Ħ, ŧ, Ŧ, ł, Ł
- Other non-decomposing letters: the dotless ı, eth ð, Ð, and kra ĸ
- Ligatures and digraphs: æ, Æ, œ, Œ, ß
Because the Unicode Character Database records no decomposition for any of these, NFD normalization passes them through untouched. Try the usual two-line script on Łódź and you get back Łódź. Remove Accents from Text carries an explicit fold table for those letters, with every entry verified against the Unicode Character Database, so ø becomes o, ł becomes l, đ becomes d, and the result is actually usable in an ASCII context.
How to Use Remove Accents from Text
The tool is designed to do one thing in three steps, and it does not require any setup, account, or upload — the page runs entirely in your browser, so nothing leaves your machine.
- Paste the text that contains accented or special Latin characters into the input box. Names, addresses, CSV columns, slug candidates, and product titles all work equally well.
- Choose your options: decide whether ligatures and letters like ß and þ should be spelled out (æ→ae, œ→oe, ß→ss, þ→th), and whether non-Latin scripts should be kept or stripped. The transliteration toggle is on by default because data cleaning usually wants these, but you can switch it off to keep the original letters while still stripping true diacritics.
- Click Remove accents, then check the change count shown right under the output and copy the cleaned result. The change count is exact, so you can verify the tool actually did something on long pastes and confirm zero changes if the input was already clean.
You can run the result back through the tool a second time and you will see a change count of zero. That is by design — the operation is idempotent — and it makes the tool safe to drop into a repeated pipeline without drift, even if the same text is processed twice. Precomposed and decomposed input such as the single code point é and the two code points e + ́ also produce identical results, because the tool recomposes to NFC after stripping marks, so it does not matter how your source stored the character.
What the Tool Will Not Touch (and Why That Matters)
Just as important as what the tool does is what it refuses to do. Greek and Cyrillic letters decompose in Unicode exactly the way accented Latin does: ά decomposes into α plus U+0301, ё decomposes into е plus U+0308. A naive mark-stripper quietly rewrites ά to α and ё to е, corrupting text in scripts it was never asked to clean. Remove Accents from Text only removes marks attached to Latin base letters, so Greek, Cyrillic, Chinese, Japanese, Korean, Devanagari, and emoji pass through byte-for-byte. Devanagari vowel signs, which are grammatically letters rather than accents, are never treated as removable marks.
An optional strict mode can delete non-Latin characters outright when you genuinely need ASCII-only output. It is off by default, because the more common intent is to clean Latin-script text while leaving foreign-language passages intact.
| Input type | Example | Plain NFD + strip | Remove Accents from Text |
|---|---|---|---|
| Accented Latin | café, señor, Zürich | cafe, senor, Zurich | cafe, senor, Zurich |
| Non-decomposing Latin | Łódź, ø, đ, þ | Łódź, ø, đ, þ (unchanged) | Lodz, o, d, th (þ with transliteration toggle on) |
| Ligatures and digraphs | æ, œ, ß | æ, œ, ß (unchanged) | ae, oe, ss (toggle on) |
| Greek and Cyrillic | ά, ё | α, е (corrupted) | ά, ё (preserved) |
| Non-Latin scripts and emoji | 中, 😀 | 中, 😀 (preserved) | 中, 😀 (preserved) |
The Greek and Cyrillic row is the one that catches most people off guard. A tool that silently rewrites ά to α is not just unhelpful, it is destructive — the Latin letter α looks similar but is a different code point, and the meaning can shift entirely when the script is changed. Skipping those scripts is the safer default for the common case, and the strict mode is opt-in rather than the default behavior.
Transliterations vs Accent Removal
Converting ß to ss is not the same kind of operation as converting é to e. The first is spelling one letter as two; the second is removing a mark that was added to a base letter. Remove Accents from Text keeps the two on principle, and the transliteration toggle is named, documented, and separated so users can see exactly when they are getting a transliteration and when they are getting a true accent strip. The toggle defaults on because data cleaning almost always wants ß→ss, æ→ae, œ→oe, þ→th and their capitals, but you can switch it off to keep those letters intact while still stripping true diacritics. The page labels the difference in plain language rather than blurring the line between the two, which matters when an audit trail needs to explain exactly what changed.
When This Kind of Cleanup Actually Saves You
The classic use cases are practical, not theoretical. You have a contact list exported from a system that stores names with full Unicode and you need to deduplicate against a legacy system that only stores ASCII. You are building URL slugs and Warszawa or São Paulo keep breaking your slug regex. You have a product catalog where Zürich, Québec, and Łódź all need to be searchable as plain ASCII for a search engine that has not been tuned for diacritics. You are normalizing user input before feeding it into a regex, a database index, or an API that does not speak Unicode. In all of these the fix is the same: get a clean Latin-letter version the downstream system can actually handle.
The tool also fits inside repeated jobs. Because the output is idempotent and the input limit is one million characters, you can include it in a script that processes a generated CSV every night and never has to special-case the same letter twice. There is no upload to track, no account to provision, and no risk of the data ever leaving your browser tab.
Limits Worth Knowing Before You Start
Remove Accents from Text is a Latin-script diacritic remover with documented transliterations, not a general transliterator. It will not romanize Russian, pinyin Chinese, or convert Greek to Latin letters. Specialist letters like ð and ĸ are folded by convention rather than by any Unicode rule, and the page discloses that rather than hiding it. The input cap is one million characters and processes in a single linear pass, so even very large pastes return instantly, but it is worth knowing the limit if you are piping a whole database dump through it. Finally, the tool only addresses Latin-script text cleanup — if your data needs case correction, slug formatting, or punctuation removal, you would pair it with a separate, focused tool rather than expecting this one to do everything.