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Text Cleaner & Normalizer

Text Cleaner & Normalizer

Clean, normalize, and format text with whitespace cleanup, line ending conversion, and comprehensive text processing

Text Input & Options

Text Cleaning Examples & Use Cases

HTML Content Cleanup

Remove HTML tags and clean web-scraped content.

Input: <h1>Welcome!</h1> <p>Visit https://example.com</p>    <div class="footer">© 2024</div>  Cleaned Output: Welcome! Visit © 2024  Operations Applied: ✅ Removed HTML tags ✅ Trimmed whitespace ✅ Normalized line endings

Email & URL Removal

Clean personal information from public content.

Input: Contact john@example.com or visit https://our-website.com for more info. Call (555) 123-4567 today!  Cleaned Output: Contact  or visit for more info. Call  today!  Perfect for anonymizing content!

    Examples

    Apply the default whitespace cleanup

    Input
      Alpha   beta  

	Gamma	 delta
    Output
    Alpha beta
Gamma delta

    The default sequence normalizes CRLF, trims lines, converts tabs to spaces, collapses repeated spaces, and removes the empty line.

    Remove web markup and contact strings

    Input
    <h1>Welcome</h1>
Contact dev@example.com
Visit https://example.com now
    Output
    Welcome
Contact
Visit now

    With HTML tag, email, and URL removal enabled, the remaining lines are trimmed and normalized by the default options.

    Apply a custom regex before title case

    Input
    Pattern: \d+
Replacement: ID
Case: title case

Text:
ticket-123
ticket-456
    Output
    Ticket-id
Ticket-id

    The global digit replacement runs before the component's title-case pass, which lowercases the remainder of each matched word.

    About this tool

    Text Cleaner & Normalizer applies configurable browser-side transformations to pasted text or loaded .txt, .csv, .md, and .log files. Its default pass normalizes line endings to LF, trims each line, replaces tabs, collapses repeated spaces, removes empty lines, and performs a final extra-spacing cleanup.

    Advanced options can remove duplicate lines, HTML-like tags, URLs, email addresses, numbers, special characters, or non-ASCII characters. The tool can also apply a global custom regular-expression replacement and convert the final result to lowercase, uppercase, or the component's word-based title case.

    Results include before-and-after text, character and line counts, whitespace and empty-line changes, detected and target line endings, processing time, and an ordered list of operations. The cleaned text can be copied or downloaded as a timestamped .txt file.

    How to use

    1. Add the source text

      Paste text, choose a built-in sample, or load a supported plain-text file.

    2. Choose basic normalization

      Configure line trimming, empty-line removal, repeated-space cleanup, and LF, CRLF, or CR line endings.

    3. Configure advanced transforms

      Optionally remove content classes, normalize case, deduplicate lines, or enter a global regular-expression replacement.

    4. Clean and inspect

      Run Clean Text, compare the result and statistics, then copy or download the cleaned text.

    Use cases

    Normalizing copied content

    Remove inconsistent indentation, blank lines, tabs, and line endings introduced by copy and paste.

    Preparing ingestion data

    Standardize plain text before database import, search indexing, text analysis, or API submission.

    Redacting common contact strings

    Remove URL and email patterns from sample or public text before further processing.

    Applying repeatable regex cleanup

    Run one global find-and-replace rule together with whitespace and case normalization.

    Common mistakes

    Mistake:Using the HTML-tag regex as a general-purpose HTML parser or sanitizer.

    Fix:Use an HTML parser or maintained sanitizer for structured or untrusted HTML; the component only removes text matching <...>.

    Mistake:Removing Unicode when the goal is merely to normalize punctuation.

    Fix:Leave Remove Unicode characters off unless deleting every non-ASCII code unit is acceptable, including accented letters and symbols.

    Mistake:Entering an invalid custom regular expression and assuming the rest of the cleanup failed.

    Fix:Check the Applied Operations list for the custom regex error; other enabled transformations can still run.

    Mistake:Expecting title case to follow language-aware editorial rules.

    Fix:Review the result manually because the implementation applies a simple word regex and lowercases the rest of each matched token.

    Frequently asked questions

    References & standards

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