HTML to Text Converter

By Bogdan Sandu · Published 9 June 2025 · Last updated 13 September 2026

Transform HTML code into clean, readable text. This lightweight HTML to Text Converter parses your markup with the browser's own HTML parser, drops the tags, and hands back the text underneath.

What the tool does:

Removes every HTML tag, and strips <script>, <iframe>, <object> and <embed> blocks together with their contents
Decodes entities like &,   and – back into real characters
Keeps the line breaks from your source, so HTML written one element per line stays readable
Accepts up to 500KB of markup per conversion
Runs entirely in your browser: nothing is uploaded, nothing is stored

How to Use This HTML to Text Converter

Paste your HTML into the top box
Click Convert to Text
Review the plain text in the bottom box
Click Copy Result to grab it, or Clear to start over

A quick before and after:

<h2>Order &amp; shipping</h2>
<p>Ships in <strong>2&ndash;3 days</strong>.</p>
<ul>
  <li>Free returns</li>
  <li>Tracked delivery</li>
</ul>

Order & shipping
Ships in 2–3 days.

  Free returns
  Tracked delivery

Two things to know before you paste: minified HTML with no line breaks comes out as one long line, and <style> blocks are not stripped, so remove those first if your source carries inline CSS. For anything past a single document, the library section further down is the better route.

Typical jobs: building a plain text alternative for an HTML email, pulling readable copy out of scraped pages, cleaning content before a CMS migration, or feeding clean text into an NLP pipeline.

What Is an HTML to Text Converter

An HTML to text converter is a software utility that strips markup, tags, and encoded entities from a document while keeping the readable text underneath.

It works on the raw source of a page or file, not on a rendered screenshot.

The output is plain text. No formatting, no styling, nothing a browser would need to draw a layout.

People confuse this with a few other things it is not.

Not a renderer. A renderer draws the page visually, this discards the visual layer entirely
Not a screenshot tool. There is no image, only characters
Not an OCR engine. OCR reads pixels, an HTML to text converter reads markup that already contains text

The value shows up anywhere raw markup needs to become something a human, a script, or a machine learning pipeline can actually use.

Mailchimp automatically generates a plain text version of every HTML email campaign sent through its platform. It exists mostly for inboxes and mail clients that reject rich formatting.

That single use case (email fallback content) is one of dozens where the same underlying process applies.

HTML to Text vs HTML to Markdown

Plain text output keeps only the words. Markdown output keeps the words plus a lightweight version of the structure, headings, bold, links, and lists rendered as symbols instead of tags.

The two targets solve different problems, and picking the wrong one causes headaches later.

What plain text keeps:

Sentences, paragraphs, and raw wording
Nothing else. No emphasis, no heading levels, no link syntax

What Markdown keeps:

Everything plain text keeps
Heading levels marked with hash symbols
Bold and italic markers, list bullets, and link syntax

Pandoc, the widely used open-source document converter, treats these as two separate output formats rather than variations of the same thing.

Pick plain text for search indexes, spam filters, and NLP pipelines that only care about words.

Pick Markdown for documentation systems, static site generators, or anywhere the structure needs to survive the trip.

If you need the reverse direction, turning Markdown back into HTML, a dedicated Markdown to HTML tool handles that conversion instead.

How an HTML to Text Converter Works

Every converter runs the same rough sequence: load the markup, strip the tags, decode the entities, clean up the whitespace, return the text.

The details of each step vary a lot between tools, and those details decide whether the output is usable or garbled.

Parser-Based vs Regex-Based Extraction

Regex-based extraction matches tag patterns with regular expressions and deletes anything that looks like a tag.

It is fast and simple, and it breaks on nested tags or malformed HTML more often than people expect.

Parser-based extraction: builds a real document object model tree from the markup first, then walks that tree to pull out text nodes.

Regex-based extraction: skips the tree entirely and treats the whole document as a string to pattern-match against.

Mozilla's Readability.js, the library behind Firefox's Reader View, uses full DOM parsing rather than regex matching. That is part of why it survives messy real-world pages that regex-only tools choke on.

Entity Decoding

Common entity conversions:

& becomes an ampersand
  becomes a plain space
< and > become the less-than and greater-than characters

A converter has to reverse this encoding, or the output is full of literal entity codes instead of the characters they represent.

Decoding the same entity twice corrupts the text instead of fixing it, which is a more common failure than most people assume.

Whitespace Normalization

Raw HTML is full of whitespace that means nothing to a browser and would look like chaos in plain text.

Indentation, tabs, and line breaks in the source get collapsed, because a browser already ignores most of them when rendering.

Multiple consecutive spaces collapse into one
Line breaks inside a single paragraph tag get removed or replaced with a space
Breaks between block-level elements (paragraphs, divs, list items) get preserved as actual line breaks

Supported Input Formats and Character Encodings

A converter has to correctly identify two things before it touches the content: the markup version and the character encoding.

Get either one wrong and the output turns into garbled symbols instead of readable text.

Encoding and markup adoption:

UTF-8 encodes 98.5% of all websites tracked by W3Techs, making it the default assumption for any modern converter
HTML5 is the markup language behind 95.7% of websites currently online, per the same survey
ISO-8859-1, the next most common encoding after UTF-8, trails at just 1.1% of sites. That gap is wide enough that most tools treat it as a fallback rather than a target

XHTML documents follow the stricter syntax rules of XML, which means every tag has to close properly or parsing fails outright.

CDATA sections sit inside markup specifically to hold content a parser should not touch. Script blocks and inline data are common examples, and a converter is supposed to skip over them rather than extracting their raw contents as readable text.

Malformed HTML (unclosed tags, mismatched nesting) is the most common reason a converter produces broken or incomplete output. It is worth testing against before trusting a tool with production content.

How Links, Tables, and Lists Are Handled in Plain Text

Structural HTML elements do not survive the trip to plain text the same way.

Each one gets flattened according to rules the converter defines, and those rules differ from tool to tool.

Three outcomes for a hyperlink:

Dropped entirely, only the link text remains
Inlined, the URL appears right next to the text in parentheses or brackets
Converted to a footnote, with a numbered reference in the body and the full URL listed separately

Tables lose their visual grid the moment they become plain text.

Rows typically become lines, and columns within a row get separated by spaces or tabs. That works for narrow tables and turns unreadable fast for wide ones.

If you need the extracted rows in a structured file afterward, run that same table through a dedicated table to CSV converter. It keeps the rows and columns intact instead of collapsing them into loose text.

Lists fare better than tables.

Unordered list items usually keep a hyphen or asterisk marker in place of the bullet
Ordered list items keep their numbering
Nested lists keep relative indentation so the hierarchy is still visible

Choosing a Conversion Method: Online Tool, Library, or Script

Three practical paths exist for running an HTML to text conversion, and each one fits a different volume of work.

Matching the method to the job matters more than picking the "best" one in the abstract.

Method	Setup Effort	Batch Capability	Best For
Online tool	None, works in a browser	One file at a time	Quick, occasional conversions
Code library	Install a package	Thousands of files in a loop	Apps and internal tools
Command-line script	Install plus a script file	Scheduled, unattended runs	Automated pipelines

An online tool asks nothing of the person using it, paste the markup, get text back, done.

It falls apart the moment the job involves more than a handful of documents.

Code libraries slot directly into backend code, which means an application can batch-process thousands of pages overnight without a person clicking anything.

Some services also expose conversion through a callable API, which suits teams that would rather send a request than manage a dependency.

Command-line scripts sit between the two, scriptable enough for a cron job, simple enough to run by hand when needed.

Best HTML to Text Libraries by Language

Every major programming language has at least one mature option for this, and the right pick usually depends on what else the project already depends on.

Python Libraries

BeautifulSoup, paired with a parser like lxml or html.parser, for general-purpose extraction with a lot of control over output
html.parser, built into the Python standard library, for lightweight jobs that should not add a dependency
html2text, for output that reads like Markdown rather than raw plain text

from bs4 import BeautifulSoup

soup = BeautifulSoup(html, "html.parser")
for tag in soup(["script", "style"]):
    tag.decompose()
text = soup.get_text(separator="\n", strip=True)

BeautifulSoup4 logged over 400 million downloads in the past 30 days on PyPI, according to pepy.tech. That says something about how often Python developers reach for it by default.

html2text pulled in 14.1 million downloads in a single month on its own, per PyPI Stats. A smaller number, but still evidence of steady, dedicated use.

The two rarely compete directly. BeautifulSoup is the general parser, html2text is the specialist for Markdown-flavored output.

JavaScript and Node.js Libraries

The html-to-text npm package pulls roughly 11 million downloads a week according to the npm registry, making it the default choice in most Node.js projects that need this.

It runs server-side, written for JavaScript environments rather than the browser, and does not depend on an actual DOM implementation to function.

import { convert } from "html-to-text";

const text = convert(html, {
  wordwrap: false,
  selectors: [{ selector: "a", options: { ignoreHref: true } }],
});

Handles tables, lists, and links with configurable output rules
Works well inside Node-based email pipelines generating plain text fallbacks
Ships with sensible defaults, so most projects need little to no configuration

Java and Ruby Libraries

Java and Ruby both lean on a single dominant library rather than splitting attention across several options.

jsoup (Java):

Pros: CSS-style selectors familiar to anyone who has written frontend code, active maintenance, tolerant of malformed HTML
Cons: one more dependency for projects that do not already touch HTML anywhere else

Nokogiri (Ruby):

Pros: fast, built on libxml2, handles HTML and XML documents equally well
Cons: relies on native extensions, which occasionally complicates deployment on restrictive hosts

Neither library is a close call for its language. Most Java and Ruby projects default to one of these two without much debate.

Where HTML to Text Conversion Is Used

The same underlying process shows up in four very different corners of software: email, search and scraping, accessibility, and machine learning.

Each one asks the converter to solve a slightly different problem.

Where conversion actually runs:

Email systems generating a plain text fallback next to the HTML version
Web scraping pipelines pulling readable content out of crawled pages
Screen readers and other assistive tools consuming the stripped-down text
NLP preprocessing steps that feed clean text into a model

The Common Crawl Foundation, a nonprofit that archives the public web, extracts plain text from every page it crawls into separate WET files built specifically for machine learning use. The raw HTML lives apart, in its WARC files.

A single monthly snapshot from that project covers more than 2 billion pages. That gives some sense of scale for how often this conversion runs behind the scenes rather than in front of a user.

On the accessibility side, NVDA is now the most commonly used screen reader overall, used regularly by 65.6% of respondents to WebAIM Screen Reader User Survey #10.

Every one of those users depends on software that reads content in a linear, text-based order. That is exactly what an HTML to text converter produces once tags and layout are stripped away.

Deeper guidance on structuring pages for that audience lives in most web accessibility resources. Text order and semantic markup both affect how well a screen reader can follow a page.

Email adds its own twist. Apple Mail and Gmail together account for close to 90% of all email opens tracked by Litmus. A plain text fallback mostly has to render correctly in just two environments rather than dozens.

When HTML to Text Conversion Does Not Apply

Conversion fails or simply does not make sense in a handful of specific situations.

Knowing them upfront saves time debugging output that was never going to work.

Where it breaks down:

Content that only exists after JavaScript runs, since the raw HTML source never contains it
Layouts where position and alignment carry meaning, financial statements and forms being the clearest example
Workflows that need the original markup preserved for later reprocessing
Documents so malformed that a parser cannot build a usable tree at all

The first item on that list is the big one in practice.

JavaScript now runs on 98.9% of all websites, per W3Techs. A growing share of those sites render their actual content client-side rather than shipping it in the initial HTML response.

Single-page applications built with frameworks like React or Vue often deliver little more than an empty shell and a script tag in their raw source. A converter pointed at that source pulls back almost nothing useful.

Fetching the page through a headless browser first, so JavaScript has a chance to execute, is the usual workaround. At that point the task has shifted from simple conversion to full page rendering.

Visual layout is the second recurring failure point.

A converter flattens a two-column invoice into a single stream of lines. The relationship between a line item and its price can get lost the moment the columns collapse.

How to Convert HTML to Text Step by Step

The process is the same regardless of which method from earlier you pick, only the tooling changes.

Confirm the source is valid HTML and check its declared character encoding before touching the content
Choose a method that matches the volume: an online tool for one file, a library for a script, a scheduled job for recurring batches
Run the conversion and watch for how it handles entities, since this is where corrupted output shows up first
Check the tag stripping result against the original, confirming links, tables, and lists resolved the way you expected
Validate the final text against the source document, looking specifically for content that silently disappeared

Step three deserves extra attention.

A quick way to catch entity problems early is to search the output for a literal ampersand followed by letters. That pattern usually means something failed to decode.

Teams running this conversion on a schedule often wire it into existing automation. GitHub Actions is a common place to trigger a batch job overnight rather than running it by hand.

Skipping step five is the single most common shortcut that comes back to bite people later.

Common Mistakes During HTML to Text Conversion

Most conversion problems trace back to a small handful of recurring errors.

None of them are exotic. They just get missed because the output looks fine at a glance.

Mistake	Cause	Fix
Double-decoded entities	Running entity decoding twice in a pipeline	Decode once, log the step so it isn't repeated downstream
Garbled characters	Ignoring the declared encoding	Detect or confirm encoding before parsing starts
Leftover code fragments	Incomplete script or style tag removal	Strip script and style blocks before extracting text nodes
Silent link loss	Dropping hyperlinks with no fallback	Inline the URL or footnote it instead of discarding it

Malformed HTML causes a different kind of trouble, since a parser can silently skip content instead of raising an error.

Running suspect markup through an HTML beautifier first often surfaces broken or unclosed tags that would otherwise pass through unnoticed.

Double-decoding deserves one more mention on its own, separate from the table above.

It happens most often in pipelines with more than one processing stage. An earlier stage already decoded entities, and a later stage decodes them again without checking.

The fix is procedural, not technical: log every point in the pipeline where decoding happens, and there should only ever be one.

FAQ on HTML To Text Converter

Is HTML to Text Conversion Reversible Back to the Original Markup

No. Once tags, attributes, and styling are stripped away, that structural information is gone for good.

A plain text file carries only words, so recreating the markup means rebuilding tags and formatting from scratch, not reversing a lossless process.

What Is the Difference Between HTML to Text Conversion and OCR-Based Text Extraction

OCR reads pixels from a scanned image or photo and guesses at the characters it sees, so accuracy depends on image quality.

HTML to text conversion reads markup that already contains real characters, producing exact, deterministic output every time.

Can HTML to Text Conversion Preserve Emoji and Special Unicode Characters

Yes, as long as the source document and the converter both use Unicode-based encoding.

Emoji, accented letters, and non-Latin scripts pass through intact when entities decode correctly. Problems only appear when a converter assumes an older, narrower encoding by mistake.

Does Converting HTML to Text Affect SEO or Create Duplicate Content Issues

Not directly. Search engines index the live page, not a plain text copy generated afterward.

Duplicate content risk only shows up if the extracted text gets republished somewhere else on the open web without changes or attribution.

Do HTML to Text Converters Handle JavaScript-Rendered Pages by Default

Most do not. A standard converter reads the raw source, so content injected later by JavaScript never appears.

Pairing the converter with a headless browser tool like Puppeteer, Selenium, or Playwright renders the page first, then extraction works normally.

How Large a File Can a Typical Converter Process Before Performance Drops

Most libraries handle documents comfortably up to a few megabytes without issue.

Performance drops sharply on deeply nested or malformed markup. Parser-based tools build a full document tree before extracting anything, which costs more memory on larger files.

Where Does an HTML To Text Converter Break Down?

An HTML to text converter breaks down at three predictable points: unresolved character encoding, content that exists only after JavaScript executes, and markup so malformed that no parser can build a usable document tree.

Fixing a broken conversion follows a specific order, and skipping ahead wastes more time than it saves.

Verify source encoding first
Check for malformed or unclosed tags second
Test for JavaScript-dependent content third

Encoding problems corrupt every character downstream, so isolating them before structural issues get attention saves a rewrite later.

Routing a page through a headless browser before conversion closes the JavaScript gap. That trade accepts a slower rendering pipeline with its own memory and hosting cost in exchange for complete text.

Readers handling the reverse workflow, turning a formatted document into publishable markup, typically move next to a dedicated Word to HTML converter.