Normalize Unicode Text Online
Text copied from social media, PDFs, and external databases often carries invisible encoding problems. A letter like e with an accent might be stored as two separate characters. Fancy bold or italic Unicode fonts break search indexes and screen readers. Paste your text here to flatten everything back to clean, standard characters. Nothing is uploaded.
Related text cleaning tools
How it works
Paste your text into the input panel and it normalizes automatically. Toggle the options you need before running, or turn off auto-run and hit the run button manually to control when processing happens. Each toggle targets a specific Unicode problem independently so you can run one fix or all six in the same pass.
The five options
Flatten Fancy Fonts
Converts mathematical alphanumeric symbols, circled letters, and Unicode mathematical notation back to standard A-Z characters. Text written in 𝗯𝗼𝗹𝗱, 𝘪𝘵𝘢𝘭𝘪𝘤, or 𝕗𝕒𝕟𝕔𝕪 Unicode styles looks readable to humans but is made up of characters from mathematical symbol blocks, not actual letters. Search engines index them incorrectly, databases store them inconsistently, and screen readers either skip them or read out their full Unicode names. This also covers mathematical notation pulled from academic papers or scientific documents where symbols were used inline with regular text.
Strip Accents
Removes diacritical marks from accented characters. Café becomes cafe, résumé becomes resume, naïve becomes naive. Use this when your target system only accepts standard ASCII letters or when you need accent-free versions for URLs, filenames, or database keys. The letters themselves stay intact, only the accent marks are removed.
Fix Hidden Mismatches
Resolves a problem most people never know exists. Some accented characters like é can be encoded two different ways: as a single precomposed character (U+00E9) or as a base letter e followed by a combining accent mark (U+0301). Both look identical on screen but are different byte sequences. This causes string comparisons, search functions, and Ctrl+F lookups to miss matches that should be found. This toggle forces all characters into NFC form, one consistent encoding, so comparisons work correctly.
Standardize Punctuation
Replaces typographic punctuation with plain ASCII equivalents. Word processors automatically convert straight quotes into curly smart quotes, hyphens into em dashes, and three dots into ellipsis characters. These look better in print but cause problems in code, config files, and systems that only expect ASCII punctuation. This toggle converts them all back to their basic equivalents.
Remove Zalgo
Strips stacked combining characters used to create glitch text effects. Zalgo text piles dozens of combining accent marks on top of single letters to make text appear to bleed and distort. It is sometimes used in social media posts and user-generated content. The combining marks are valid Unicode characters which makes them hard to catch without dedicated processing. This toggle strips the stacking while preserving the base letters underneath. If the cleaned text still has leftover whitespace or spacing issues, the remove empty whitespace tool handles those in a follow-up pass.
Where Unicode problems come from
Social media bios and posts are the most common source of fancy font problems. Platforms do not support bold or italic text natively so users paste in Unicode mathematical characters that render as styled text. When you scrape or copy that content into a database or document, the mathematical symbols come with it.
Copy-pasting between operating systems and applications creates hidden mismatches. macOS, Windows, and Linux handle some character compositions differently by default. Text that looks identical from both sources can contain different byte sequences that only reveal themselves when compared or searched programmatically.
PDF extraction produces accent and diacritic issues. Many PDF renderers decompose accented characters into their component parts on extraction, turning é into e plus a combining mark. The output looks correct but the encoding is inconsistent with text from other sources.
Multilingual content pulled from external APIs and databases frequently arrives with mixed normalization forms, especially when the source system did not enforce a consistent encoding standard on input.