HTML to Plain Text Converter

Extract plain text from HTML by removing all markup and scripts.

Input

Output

Examples

Web page content

<div><h1>Title</h1><p>This is a <strong>paragraph</strong> with formatting.</p></div>

Email template

<html><body><p>Hello,</p><p>Click <a href="#">here</a></p></body></html>

Understanding HTML to Text Conversion

HTML to text conversion extracts readable content from HTML documents by removing all markup, scripts, styles, and formatting. The result is clean, plain text suitable for analysis, search indexing, or display in text-only contexts. This process is essential when HTML structure is unnecessary and only the content matters.

The conversion process parses HTML using browser DOM APIs, traversing the document tree to extract text nodes while discarding tags, attributes, and non-content elements like scripts and styles. Line breaks and spacing are preserved to maintain readability, ensuring that paragraphs and lists remain distinguishable in plain text.

Unlike simple regex-based strippers, DOM-based conversion handles nested tags, malformed HTML, and complex structures gracefully. The browser normalizes HTML during parsing, ensuring that even poorly formatted markup is processed correctly. This robustness makes the tool reliable for real-world web content.

This tool operates entirely in the browser, ensuring data privacy. HTML content is processed locally without being sent to external servers. Users can extract text from sensitive documents confidently, knowing that their content remains secure.

Practical Applications

Content analysis and text mining workflows require plain text for natural language processing. When analyzing web content, search relevance, or sentiment, HTML tags introduce noise. Stripping HTML produces clean text suitable for tokenization, keyword extraction, and statistical analysis.

Search indexing systems extract text from HTML to build searchable databases. Removing markup ensures that search engines index content, not code. This tool allows developers to preview how content appears to search crawlers, verifying that meaningful text is extracted.

Email systems often need plain-text versions of HTML emails for compatibility. Text-only email clients cannot render HTML, so providing plain-text alternatives ensures accessibility. This tool generates readable text from HTML email templates, simplifying multi-format email delivery.

Accessibility testing benefits from HTML to text conversion. Screen readers and assistive technologies extract text from markup, and understanding how content appears as plain text helps identify accessibility issues. This tool simulates text extraction, revealing whether content is meaningful without visual formatting.

Challenges and Limitations

Stripping HTML loses all formatting and structure information. Bold, italic, headings, and lists are reduced to plain text, making visual hierarchy invisible. Users needing to preserve some structure should consider Markdown conversion, which retains semantic formatting.

JavaScript-generated content is not captured during conversion. If HTML relies on client-side rendering, the tool only sees static markup. For dynamic pages, server-side rendering or headless browser tools are needed to capture generated content before extraction.

Whitespace handling can produce unexpected results. HTML collapses multiple spaces and ignores most whitespace, but plain text may preserve it differently. The tool attempts to maintain readability, but users should verify that spacing meets expectations.

Non-text content like images, videos, and iframes is omitted entirely. Alt text for images may be extracted if present, but visual or multimedia content cannot be represented in plain text. Users should be aware that conversion focuses solely on textual content.

Best Practices

Use semantic HTML for better text extraction. Properly structured HTML with headings, paragraphs, and lists produces more readable plain text. If you control the source HTML, improving its structure before conversion yields cleaner results.

Verify that extracted text is complete and accurate. Review the output to ensure that important content is not lost during stripping. Edge cases like deeply nested tags or unusual markup may require manual adjustment.

Combine HTML stripping with text normalization for analysis. After extraction, apply whitespace normalization, case folding, or punctuation removal to prepare text for processing. This layered approach separates extraction from transformation, improving modularity.

Consider Markdown conversion if structure is important. HTML to text loses formatting, but HTML to Markdown preserves headings, lists, and links. Choose the appropriate conversion based on whether structure matters for your use case.

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