Web Page Text Extractor
Extract clean, readable text from any web page URL. Strip HTML, ads, navigation, and clutter — get just the pure article content. Free, no sign-up, no limits. Powered by Jina Reader.
Pure Text Extraction
Strip all HTML tags, CSS, JavaScript, navigation menus, ads, sidebars, and footers. Get only the main article content as clean plain text.
LLM-Ready Output
Extracted text is optimized for AI consumption — feed it into ChatGPT, Claude, DeepSeek, Gemini, or any LLM for analysis, summarization, or knowledge base building.
Real-Time Stats
See character count, word count, and line count instantly. Every extracted text comes with detailed statistics to help you estimate token usage and reading time.
Free & Unlimited
No sign-up, no API keys, no rate limits. Extract text from as many URLs as you need. Copy to clipboard or download as .txt file in one click.
Who Uses a Web Page Text Extractor
Extract web content to build training datasets, create RAG (Retrieval-Augmented Generation) corpora, and feed context into LLM prompts. Over 60% of AI engineers use text extraction tools weekly.
Pull clean article text from multiple sources for comparison, analysis, and citation. Remove noise from cluttered web pages to focus on what matters — the actual content.
Extract competitor content for gap analysis, audit article structure, and build content strategies informed by real data. Plain text makes comparison effortless.
Save web articles as clean text files for import into Obsidian, Notion, Logseq, Roam, or any personal knowledge management system. Build your second brain from the web.
Use the same r.jina.ai endpoint in n8n, Zapier, Make, or custom scripts. One API call extracts any URL to plain text for downstream processing.
Research multiple sources without tab clutter. Extract key quotes, facts, and references as plain text for your writing workflow. No distractions, no formatting issues.
Frequently Asked Questions
What is a web page text extractor?
A web page text extractor is a tool that takes a URL and extracts the main article content — removing HTML tags, navigation menus, ads, sidebars, footers, and other clutter. The result is clean, readable plain text that can be used for AI analysis, research, note-taking, or content repurposing.
Is this web page text extractor free?
Yes, completely free. No sign-up, no usage limits, no API keys needed. All processing happens client-side — your URLs and extracted text never leave your browser.
How is this different from the HTML to Markdown converter?
The HTML to Markdown converter preserves formatting (headings, lists, links) in Markdown syntax. The Web Page Text Extractor strips everything down to pure plain text — useful for word counts, AI token limits, text analysis, and scenarios where you need just the raw content without any formatting.
What can I use extracted text for?
Extracted text is ideal for: feeding into LLMs (ChatGPT, Claude, DeepSeek) for analysis, building training datasets, content research and comparison, saving articles for offline reading, generating summaries, creating knowledge base entries, and SEO content audits.
Does the text extractor remove ads and navigation?
Yes. The Jina Reader API intelligently extracts the main content of a web page while filtering out ads, navigation bars, sidebars, footers, and other non-content elements. The result is clean, focused text from the article body.
How accurate is the text extraction?
Jina Reader uses advanced AI to identify and extract the main content of a web page. It handles news articles, blog posts, documentation, and research papers with high accuracy for most modern websites. Results vary based on page structure complexity, but the vast majority of pages produce clean, usable text.
Can I extract text from any URL?
You can extract text from any public web page URL. The tool works with articles, blog posts, documentation, news sites, research papers, and most standard web content. Some pages behind login walls, paywalls, or with heavy JavaScript rendering may not work perfectly.
How do I estimate token count for LLM usage?
A rough rule of thumb: 1 token ≈ 4 characters in English text. Divide the character count by 4 to estimate tokens. For example, 10,000 characters ≈ 2,500 tokens. The exact ratio varies by tokenizer — OpenAI's tokenizer averages ~3.5 chars per token, while Claude averages ~4.2 chars per token.