PDF to Text · for Claude

PDF to Text for Claude

Get clean, paragraph-structured text out of any PDF and feed it to Claude — perfect for 200K-token deep reads, Projects, and long research briefs.

Open PDF to Clean Text

Claude's context window

Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Haiku all accept 200K tokens — roughly 500 pages of clean text or a 150K-word manuscript.

Claude tokens run a touch denser than GPT's. Estimate ~3.5 characters per token for English; a 500-page novel is around 200K tokens cleaned.

Want exact numbers? Count tokens for Claude →

The workflow

Convert your PDF in the browser — OCR triggers automatically for scans.
Paste the cleaned text into a Claude conversation, or attach it as a Project knowledge file.
For Projects, name the file descriptively — Claude's retrieval favors filename signals.
Ask Claude to cite paragraph numbers if you want easy verification.

Common pitfalls

Assuming Claude's larger context means cleaning doesn't matter — noise still hurts retrieval inside Projects.
Letting hyphenated line breaks through; they show up as misspellings in citations.
Sending PDFs with embedded fonts that scramble copy-paste — render to text first.

Tool

PDF to Clean Text

Extract clean, AI-ready text from any PDF.

Frequently asked

Should I use Claude Projects or paste text into a chat?

Projects are best for documents you'll re-query over many sessions. One-off briefs are faster to paste directly. Either way, clean the text first.

Does Claude really need 500 pages of clean text or can I dump everything?

You can dump everything, but Claude's lost-in-the-middle effect means information at the 30–70% mark is recalled less reliably. Cleaner, tighter context = better recall.

Is in-browser OCR private?

Yes. The PDF never leaves your device — Tesseract runs as WebAssembly inside your browser tab.

PDF to Text for other models