Plain Text Converter: Remove All Formatting
Transform formatted text into clean, plain text with our formatting removal tool. Whether you're cleaning copied content, preparing text for code, or ensuring consistent formatting, our tool strips away all styling while preserving your essential content.
Strips all rich text formatting from pasted content instantly — removes bold, italic, underline, color, font changes, and other inline styles, leaving clean plain text.
Eliminates hidden formatting markers that survive copy-paste from Word, Google Docs, email clients, and web pages — characters and whitespace that look invisible but cause rendering issues in downstream systems.
Normalizes Unicode to ensure consistent character encoding — replaces smart quotes, em-dashes, non-breaking spaces, and other typographic characters with their plain ASCII equivalents where appropriate.
Handles multiple space normalization — collapses runs of multiple spaces into single spaces, removing the invisible extra whitespace that accumulates in formatted documents.
Processes any volume of text in a single paste with no character limit.
Free with no account required.
How to Use
Paste your formatted text
Preview clean result
Copy or download plain text
Content Preparation
- CMS pasting
- Code comments
- Database entries
- Plain text emails
Text Cleaning
- Word processor content
- Rich text cleanup
- HTML text extraction
- Document conversion
Data Processing
- Input sanitization
- Data validation
- Content migration
- API text preparation
Content Systems
- CMS platforms
- Code editors
- Email clients
- Text editors
Source Formats
- Rich Text
- HTML
- Word docs
- Markdown
Always clean text pasted from Word or Google Docs before inserting it into a CMS, database field, or API payload — rich text copy-paste carries hidden XML tags, font references, and encoding markers that corrupt database records and cause rendering failures in systems that expect plain text.
When copying content from websites into a plain text editor or form field, use this tool to strip the invisible HTML-derived formatting that browsers include in clipboard text — link underlines, color codes, and span tags all survive browser-to-editor paste operations.
For data entry workflows, cleaning pasted text before submission ensures that form validators, character limit checks, and database constraints apply to the actual visible content rather than to the hidden formatting overhead that can add hundreds of bytes to a short string.
When preparing text for NLP pipelines and machine learning models, stripping formatting is a standard preprocessing step — Unicode curly quotes, em-dashes, and non-breaking spaces produce unexpected tokenization results in models trained on clean plain text corpora.
Email content pasted from a designer's draft into an email service provider's plain-text field often carries hidden formatting — strip it here before pasting to ensure your plain-text email fallback contains only readable characters.
Always keep the original formatted version before stripping — some formatting (numbered lists, code indentation, table structure) carries meaningful semantic information that plain text cannot represent, and restoring it manually is time-consuming.
Verify that special characters your content requires — em-dashes for editorial style, copyright symbols, mathematical operators — survive the stripping process in the form you need. Some normalization modes convert typographic characters to their ASCII equivalents which may not be correct for your use case.
For content that will be re-formatted after cleaning (imported into a CMS that applies its own styling, or processed by a script that adds new formatting), stripping first prevents double-formatting artifacts — old formatting colliding with new.
When stripping formatting from email content, check that hyperlinks are handled correctly — URLs embedded in rich text may appear as formatted link text with the actual URL hidden. After stripping, plain text should show the URL itself, but verify that no links have become invisible.
For large documents, spot-check several sections of the stripped output before using it downstream — some formatting strippers handle certain edge cases inconsistently, and a brief review catches any characters or structures that were not cleaned as expected.
Frequently Asked Questions
Find answers to common questions about our tools and services.
Understanding Remove Text Formatting
Text formatting removal solves one of the most persistent problems in cross-application content workflows: the invisible formatting markup that travels with text copied from rich text environments. Microsoft Word, Google Docs, LibreOffice, email clients, and web browsers all store text with associated formatting metadata — font names and sizes, color codes, bold and italic markers, margin settings, and in many cases XML or HTML tags. When this text is copied and pasted into a system that expects plain text — a database field, an API payload, a CMS content field, or a code editor — that metadata arrives as garbage characters, producing corrupted records, rendering failures, and validation errors.
The most common professional scenario is content management. Writers draft in Word or Google Docs, then paste into a CMS like WordPress, Contentful, or a custom admin panel. If the CMS input field accepts rich text and renders it correctly, the paste works fine. But if the field expects plain text — a product description stored as a VARCHAR, an email subject line, a meta description field, an API body parameter — the pasted content contains invisible formatting characters that corrupt the stored value. Stripping the formatting here before pasting eliminates those characters, ensuring the stored value is exactly the visible text and nothing else.
Data pipeline and ETL contexts face a related problem with text fields imported from spreadsheets. Excel and Google Sheets export to CSV correctly for plain text cells, but cells that were formatted with bold, colors, or special number formats can carry invisible markers in their exported values. These markers cause type conversion failures, character encoding errors, and search indexing anomalies in downstream databases. Cleaning the text through a formatting stripper before import is a reliable preprocessing step that prevents these issues.
NLP and machine learning preprocessing benefits significantly from formatting removal. Language models, text classifiers, and sentiment analysis systems are typically trained on corpora of clean plain text. Input text with Unicode typographic characters — curly quotes ("), em-dashes (—), non-breaking spaces (U+00A0), and other typographic substitutions — produces unexpected tokenization results that degrade model performance. Normalizing these characters to their plain ASCII equivalents (straight quotes, hyphens, regular spaces) is a standard cleaning step that this tool performs as part of format stripping.
Email and SMS content workflows have a specific version of this problem. Marketing emails are composed in rich text editors by copywriters, then handed to email developers who need the text content without the styling — because the email template applies its own CSS and font stack. Pasting styled text directly into the email HTML template produces double-formatting artifacts. Stripping the formatting first gives the email developer clean text they can paste into the template's text nodes without any collision between the original styles and the template's styles.