Text Cleaner
Clean messy text with checkboxes — trim spaces, strip HTML, remove special characters, normalize Unicode, and more.
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Clean Messy Text Data in Seconds
Text data is notoriously dirty: extra spaces, HTML tags pasted from websites, accented characters from international sources, emojis mixed into product names, inconsistent capitalization. DataScrub's Text Cleaner fixes all of this with checkboxes — no regex knowledge needed.
Select the operations you want, choose which columns to apply them to, and preview every change before committing. All processing happens locally in your browser.
How to Clean Text Data
- Upload your CSV, Excel, or ODS file.
- Select the cleaning operations you need (trim, strip HTML, remove emojis, and more).
- Choose which columns to apply the operations to.
- Preview changes live — the tool shows before and after for each row.
- Apply the cleaning and download your polished file.
Available Cleaning Operations
Each operation targets a specific type of text noise. You can combine multiple operations in a single pass.
- Trim whitespace — remove leading and trailing spaces.
- Normalize internal spaces — collapse multiple spaces between words into one.
- Remove line breaks — strip newlines and carriage returns from cells.
- Strip HTML tags — clean data that was scraped or pasted from web pages.
- Remove special characters — strip punctuation and symbols.
- Remove accents/diacritics — convert accented characters to their ASCII equivalents.
- Remove emojis and symbols — clear emoji and symbol characters from text.
- Convert case — lowercase, UPPERCASE, or Title Case.
- Remove numbers — strip numeric digits from text.
- Remove URLs — delete web links from cell values.
When to Use Each Operation
Trim is almost always needed — stray spaces cause lookup failures and duplicate detection problems. Strip HTML is essential for data scraped from web pages, where cells may contain hidden tags. Remove accents helps standardize international names for matching. Remove emojis is useful for product catalogs and review data where symbols interfere with sorting or filtering.
Tips for Cleaning Text
- Apply trim last — other operations like stripping HTML may leave trailing spaces behind.
- Be careful with case conversions — pick one (lowercase, uppercase, or title case), not multiple at the same time.
- Preview before applying to make sure the operations produce the result you expect.
- If your data has mixed languages, test accent removal on a small sample first to ensure it does not change meanings you want to preserve.
Frequently Asked Questions
Can I apply multiple operations at once?
Yes. Check as many operations as you need and they will be applied in a defined order: HTML strip, emoji removal, accent removal, URL removal, special character removal, number removal, line break removal, space normalization, case conversion, and finally trim. This order prevents earlier operations from creating artifacts that later ones would miss.
What's the difference between trim and normalize spaces?
Trim removes leading and trailing spaces from each cell. Normalize spaces collapses multiple spaces between words into a single space, but does not remove spaces at the start or end. Using both together gives you the cleanest result.
Will this remove characters I need?
Some operations are broad by nature — remove special characters, for example, will strip punctuation along with symbols. Always preview the result before applying. If an operation is too aggressive for your data, simply uncheck it and rely on more targeted operations instead.
Does it work with non-English text?
Yes. The tool handles Unicode text, including CJK characters, Cyrillic, Arabic, and other scripts. Accent removal specifically targets diacritical marks (like converting cafe to cafe), and does not affect non-Latin scripts. Emojis and special symbols are also removed correctly regardless of the surrounding language.