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SerpGem
Content Tool

HTML Tag Stripper

Paste HTML and extract clean plain text — perfect for meta descriptions, AI prompt input, email bodies, or anywhere tags would break things. Preserves paragraph structure and optionally inlines link URLs.

InputHTML to strip
OutputPlain text

Before

0

After

0

Tags stripped

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HTML Stripping Guide

When you need the text, not the markup

HTML makes sense for rendering — but it's noise everywhere else. Meta descriptions don't accept tags. AI prompts work better on plain text. Email clients strip them anyway. This tool extracts the text content cleanly, preserves the structure, and decodes entities so you get real characters, not &.

Structure preservation

By default we turn closing block tags (</p>, </h1>, </div>) into paragraph breaks, and <br> into single line breaks — so the output reads like text, not a run-on sentence.

Entity decoding

Decodes &amp;, &lt;, &quot;, &nbsp;, &mdash;, and numeric entities (&#8212;, &#x201D;) to real characters. Essential for meta descriptions where encoded entities look broken.

Preserve link URLs inline

Toggle 'Keep link URLs' to render anchors as 'Anchor text (https://...)'. Useful when extracting text from blog posts where the URLs matter — like auditing outbound link destinations.

Script and style removal

We strip <script> and <style> blocks entirely (including their contents) and skip HTML comments. Paste a whole rendered page and you get just the visible text.

Whitespace collapse

HTML often has indentation and multi-line tag attributes that become noise when you strip the tags. 'Collapse whitespace' merges runs of spaces and keeps at most one blank line between paragraphs.

Output length tracking

The before/after character counts tell you how dense your HTML was. A 10KB HTML blob stripping to 3KB of text is typical — the rest was markup and class names.

Pro Tips

Meta description source

If you're writing meta descriptions from blog content, strip the HTML first, then run through Text Cleaner to normalize quotes, then copy the first 155 chars.

For AI prompts

Language models waste tokens on HTML tags. Stripping first cuts input size 30–50% and often improves response quality — the model focuses on content, not markup.

Careful with templates

If you're stripping tags from a templating language (e.g., Liquid, Mustache), double-curly-braces like {{ name }} will survive — they aren't HTML. Use Find & Replace after to strip those.

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Frequently Asked Questions

Does this work on a whole HTML page source?
Yes — paste the full page source and we extract the text. <script> and <style> blocks are removed entirely, and block-level tags become line breaks so the output reads like an article outline.
What about <img> alt text?
Alt text is an attribute, not content, so it's removed along with the tag. If you need to extract all alt text, use our Image Alt Text Auditor (coming) — built specifically for that task.
Why did my inline &mdash; stay as an em-dash?
Because 'Decode HTML entities' is on by default — we convert &mdash; → —, which is usually what you want. Turn it off if you specifically need the literal &mdash; text preserved.
Can this handle malformed HTML?
Reasonably well — we use regex-based stripping which tolerates missing closing tags, weird nesting, and stray angle brackets. For truly broken HTML that needs to be parsed as a tree, a full HTML parser would be safer.