HTML Tag Remover
Remove HTML tags from text and extract clean plain text instantly. Free online HTML stripper for content migration, data cleaning, and text analysis — no signup needed.
Free HTML Tag Remover — Extract Plain Text from HTML Instantly
HTML markup wraps content in tags that tell browsers how to render it visually — but those same tags are clutter when you need the text content alone. Copying text from a web browser, exporting content from a CMS, or working with HTML stored in a database often means dealing with angle-bracket tags mixed in with the readable text. Our free HTML tag remover strips all of those markup elements, leaving only the clean text content — no <p> tags, no <strong> tags, no <div> wrappers, no attributes, no entities. Just the words.
When You Need Plain Text from HTML
Content migration is the most common large-scale HTML stripping use case. When moving content from one CMS to another, from a website to a document, or from an HTML-based export to a new platform, the content often arrives with HTML markup. A blog post stored in a database as <h2>My Heading</h2><p>First paragraph text with <strong>bold words</strong> and a <a href="...">link</a>.</p> needs to become plain text before it can be re-imported into a system that expects clean text, analyzed for word count and readability, checked for keyword density, or fed into a text processing pipeline.
Email template content is another frequent source of HTML that needs stripping. HTML emails are coded with tables, inline styles, and markup that makes the email render correctly in email clients, but when the HTML source is extracted for analysis, content auditing, or conversion to a plain text version, the markup must be removed first. Many email service providers require both HTML and plain text versions of emails — the plain text version is easily generated by stripping the tags from the HTML version and cleaning up any entity-encoded characters.
Data Cleaning and Text Analysis
Web scraping and data collection tools often capture raw HTML source or semi-formatted content with residual tags. Natural language processing tasks — sentiment analysis, keyword extraction, topic modeling, named entity recognition — require clean plain text as input, not HTML-tagged content. Running scraped web content through the HTML tag remover is a standard preprocessing step before any text analysis operations.
Database content fields sometimes accumulate HTML markup over years of editing through rich text editors. A database audit may reveal product descriptions, blog posts, or article content stored with inconsistent markup — some fields with proper HTML, others with partial tags, others with plain text. Stripping the tags and normalizing to plain text creates a consistent starting state for content review and re-editing.
Word Count and Readability Analysis
Analyzing the readable word count of HTML content requires removing the markup first. An HTML page that appears to the visitor as a 500-word article might contain 800 or more "words" when the raw HTML source is counted — because tags, attribute names, URL text in hrefs, CSS class names, and JavaScript code all count as characters and words in a character counter that does not understand HTML. Stripping the tags first ensures that word count, character count, and readability score tools measure only the actual readable content.
SEO analysis tools like readability checkers, keyword density calculators, and content structure analyzers all require plain text input. Content writers and SEO professionals who work with HTML source from CMS exports or page source code use the tag remover as a preprocessing step before running readability or density analysis, ensuring they are measuring the content their readers actually see rather than the markup scaffolding around it.
HTML Entities and Character Decoding
HTML content typically uses entity encoding for certain characters — & represents &, < represents <, > represents >, " represents ", ' represents ', and represents a non-breaking space. After stripping the HTML tags, these entity codes remain in the plain text output and should be decoded to their actual characters to produce fully readable plain text. Our tool handles this decoding step as part of the conversion, so the output contains actual ampersands and quotation marks rather than entity code strings.