Extract Text from PDF Free

Pull clean, unformatted text from any PDF into .TXT file. Preserves paragraph structure, removes headers/footers. Free, no signup, unlimited.

Key Features

Smart space insertion — synthesizes missing spaces between text items based on font-size gap analysis
Paragraph break detection — uses median line height to identify real paragraph boundaries
Multi-column layout reflow — academic papers and two-column reports come out as single-column prose
Table detection — 3+ column-aligned rows flagged as tables
Three output formats — plain text, Markdown (per-page sections), JSON (with coordinates)
Page range filtering — "2-4, 7" extracts text only from those pages
Handles rotated text via transform-matrix math (true size = √(a² + b²))
Runs in browser — pdfjs-dist extraction is instant even for 500-page documents

About PDF To Text

PDF to Text extracts the text content from a PDF as clean plain text, Markdown, or structured JSON. The extraction uses pdfjs-dist at its lowest level — we don't just grab visible strings, we analyze text item positions, font sizes, and spacing to reconstruct the original reading order. A "smart join" pass infers where spaces should go between text items (using a 25% of font-size gap threshold to distinguish "foo bar" from "foobar"), and a paragraph-break detector uses the median line height to figure out where paragraphs actually end.

What separates this from naive "dump PDF text" tools is the layout analysis. We detect multi-column layouts and reflow them into a single column, identify tables by finding 3+ column-aligned rows, and preserve paragraph breaks instead of flattening everything into one giant wall of text. The Markdown output adds per-page section headers and confidence annotations so you can see which pages had clean extraction vs. which needed heuristic guesses. The JSON output includes coordinate positions for each text block — useful for custom indexing, annotation tools, or full-text search engines.

Who Uses This Tool

Turning academic papers into searchable plain text for research notes
Extracting legal contracts into Markdown for version-controlled collaboration
Feeding PDF content to LLM pipelines for AI analysis and summarization
Building full-text search indexes over PDF document archives
Copying content from journal articles into literature review documents
Extracting data from government PDF filings for civic tech projects

How to Use Extract Text from PDF Free

Step 1: Drop your PDF into the drop zone
Step 2: Pick the output format — Plain text for copy-paste, Markdown for note-taking apps, JSON for programmatic use
Step 3: Optionally set a page range ("2-4, 7") if you only want text from specific pages
Step 4: Click Extract. pdfjs-dist pulls the text items, the layout engine groups them into lines and paragraphs, and the smart-join pass fixes spacing.
Step 5: Download the result as .txt, .md, or .json. Copy-paste the content into Word, Google Docs, Notion, or your processing pipeline.

Frequently Asked Questions

Why does plain PDF text extraction usually have garbled spacing?

Because PDFs store text as positioned characters, not as words and spaces. When you copy-paste from a PDF in many viewers, you get "foobarbaz" instead of "foo bar baz" because there are no actual space characters between the items — just positioning differences. Our smart-join pass fixes this by inferring spaces from the positional gaps (any gap larger than ~25% of font size becomes a space).

Does this work on scanned PDFs?

No — scanned PDFs are image-only and contain no text to extract. Use our OCR tool first to recognize the text, then the extraction works normally. The OCR tool also has a direct "text output" mode so you can skip the two-step process.

What's the difference between Plain Text and Markdown output?

Plain text gives you raw reading-order content with paragraph breaks preserved — best for pasting into Word or Notion. Markdown adds per-page H2 section headers, preserves heading hierarchy where detectable, and flags low-confidence extraction with annotations — best for archival, note-taking, or structured processing.

What's in the JSON output?

Structured text items with their page number, bounding box coordinates (x/y/width/height), font size, and font name. Useful for building custom PDF search engines, annotation tools, highlight-sync features, or any application that needs to map text back to its visual position in the original PDF.

Does it handle multi-column layouts (like academic papers)?

Yes. The layout engine detects two-column structures by clustering text-item X positions and reflows them into sequential reading order. Three-column brochures and newsletter layouts are trickier — the tool works on most but may produce interleaved output on unusual designs. For complex layouts, use the JSON output and reconstruct reading order manually.

Why are some text items grouped on the same line when they shouldn't be?

The line-grouping pass uses Y-proximity within a font-size tolerance to decide what's on the same line. Text with unusual leading (tight line spacing) can sometimes merge — try re-exporting with a looser grouping threshold in the advanced options, or use the JSON output which keeps items separate.