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Online HTML Tag Remover
Instantly remove HTML tags from text with this intuitive online tool. Whether you paste raw code, upload a file, or fetch a webpage by URL, it delivers a clean plain-text output—perfect for editing, analysis, or seamless integration into Excel, Power BI, or other workflows.
Clean Text Output
Example 1: Paragraph
User Guide: Perfect for cleaning rich text content while keeping plain sentences intact.
Example 2: Mixed HTML
User Guide: Useful when cleaning HTML from email templates or messages.
Example 3: Script Removal
User Guide: Ideal for removing unsafe scripts or hidden code from snippets.
Example 4: Style Removal
User Guide: Best for stripping style attributes to maintain raw text only.
About This Tool
The Remove HTML Tags tool is a practical, lightweight utility designed to extract plain text from HTML markup. It helps you remove HTML tags from text, strip inline styles and script blocks, and produce clean content ready for analysis, publishing, or copy-paste reuse.
Built for both casual users and developers, the tool supports browser-side workflows and complements server-side approaches (Python, PHP, Power Automate, Power BI). It preserves visible words while removing markup, so your text remains readable and actionable.
How to Use
- Paste: Insert HTML or rich text directly into the input area and see an instant cleaned result.
- Upload: Load a local file (HTML, .txt) to remove HTML tags from a file without copying manually.
- Fetch: Provide a page URL to fetch markup and extract visible text automatically (CORS permitting).
- Options: Toggle entity decoding, script removal, and style removal to tailor output for Excel, Power BI, or text-analysis tools.
- Export: Copy, download, or transfer the cleaned text to spreadsheets, Power Query, or automation flows.
Tip: For bulk sheets use Power Query or automated scripts; for one-off cleanup use the browser UI and copy output into Excel or Word.
Pro Tips
- Prefer parsers: Use a proper HTML parser (BeautifulSoup, DOMParser) rather than naive regex for complex or malformed HTML.
- Preserve spacing: Decode entities like when you need readable spaces after tags are removed.
- Pre-clean: Remove <script> and <style> blocks first to avoid accidental code leakage in the output.
- Integration: Export results to Power BI, Power Automate, or Excel for downstream reporting and transformations.
- Test on copies: Always run on a duplicate file when trying new options or regex patterns to avoid data loss.
FAQs
1. How do I remove HTML tags from text reliably?
Use a parser-based approach—DOMParser in JavaScript or BeautifulSoup in Python—to extract visible text safely. Parsers handle malformed markup, entity decoding, and nested tags far better than simple regex, producing more accurate plain-text results for reuse.
2. Can I remove HTML tags from text in Excel or Power BI?
Yes. In Excel use Power Query transformations or small VBA helpers; in Power BI use Power Query (M) functions to strip tags and decode entities. Both approaches scale well for sheets and let you automate cleanup in data pipelines.
3. How to remove HTML tags from text in Python?
Use BeautifulSoup to parse markup and call .get_text() for robust extraction. For quick jobs, a carefully written regex can work, but BeautifulSoup handles entities and broken HTML, making it the preferred choice for production scripts.
4. Will removing tags also remove HTML entities like ?
Not always automatically. After stripping tags you may still see entities. Enable entity decoding or use a parser that converts entities into characters to preserve spacing and readability in the cleaned text output.
5. How do I remove scripts and styles before stripping tags?
Remove <script> and <style> blocks first—either with targeted regex or using a parser that deletes those nodes. This prevents code or styling from appearing in your plain text and keeps the extraction safe and clean.
Conclusion
Removing HTML tags turns marked-up content into clean, usable text for analytics, reports, or publishing. Whether you choose a quick browser tool, Excel/Power BI workflows, or a scripted approach in Python, follow parser-first practices, decode entities, and always test on copies to preserve data integrity.