Remove Diacritics Online | Strip Accents to ASCII-Friendly Text

Remove Diacritics Online

Remove diacritics online helps convert accented characters into a plain-text version that’s easier to store, search, and match across older systems. This tool removes accents by applying Unicode normalization (NFD) and then dropping combining marks, which turns characters like “é” into a base “e” plus a removable mark. The result is especially useful when a database, legacy API, or file format expects ASCII-friendly values and rejects or mangles accented input. WizardOfAZ makes this a paste-and-convert step, which fits quick cleanup work like preparing usernames, product SKUs, or URL slugs from international text. It’s worth keeping the original text alongside the stripped version, because removing diacritics can change meaning or cause collisions between different names that become identical after stripping. After conversion, spot-check a few words that include special letters (like ñ, ø, ğ) to confirm the output matches what your destination system can actually accept.

Remove Accents From Text

Remove accents from text is often needed when a form field fails validation even though the words look correct. A stripped version can also improve matching in search systems that are not accent-insensitive, where “resume” and “résumé” might be treated as different terms. This tool uses a normalization-and-strip approach, so it removes the accent mark rather than attempting language-specific transliteration rules. That makes it predictable for data cleanup, because the same input always produces the same stripped output. For best results, paste only the content you plan to store in the restricted field (for example, a handle or ID) and keep the full accented version for display text. If your workflow includes URLs, stripping accents first and then applying URL encoding later often produces cleaner, more readable slugs than encoding every accented character.

Strip Diacritics For Csv Export

Strip diacritics for CSV export when the receiving tool misreads encodings or when downstream imports split characters incorrectly. This is common with older pipelines that assume ASCII or that apply inconsistent text encodings during import and export steps. A practical approach is to generate two columns: one column with the original accented value for humans, and one “export-safe” column with accents removed for strict integrations. When you do this, deduplication becomes important, because different names can collapse into the same stripped spelling, creating duplicate keys in CSV headers or identifier fields. Before exporting, test a small CSV sample in the destination system to confirm whether stripping is actually required, since many modern tools can handle UTF-8 correctly. If stripping is required, document the rule (NFD + remove combining marks) so the same transformation can be applied consistently in future exports and scripts.

Unicode Diacritics Remover Nfd

Unicode diacritics remover NFD refers to the technique of decomposing characters into base letters plus combining marks, then deleting the mark category. The Diacritics Remover page describes this exact method: Unicode normalization (NFD) followed by dropping combining marks to produce plain output. This is useful because it avoids maintaining language-specific mappings and instead relies on Unicode’s structured representation of many accented forms. Not every “special letter” is a base letter plus a combining mark, so some characters may not reduce cleanly to a single ASCII letter, and that’s a normal limitation of accent stripping. For systems that must remain ASCII-only, this method still produces a consistent best-effort output without introducing invisible characters or mixed normalization forms. If your application compares strings, normalizing first can also reduce “looks the same but isn’t equal” issues caused by different Unicode compositions of the same visible character.