PDF Text Extractor

Extract text, layout, and bounding boxes from any PDF — entirely in your browser. Your file never leaves this page. Powered by liteparse WebAssembly.
Drop your PDF here
or click to select a file
Max 50 MB · PDFs only · processed locally in your browser
100% local — your PDF never leaves this browser
No upload. No tracking. Powered by liteparse (Apache 2.0).

How to extract text from a PDF in your browser

  1. 1

    Drop or select your PDF

    Drag a PDF file into the upload zone, or click it to open the file picker. The file is loaded into your browser's memory only — there is no upload.
  2. 2

    Wait for the WASM engine to parse

    On first run, the WebAssembly engine loads (about 4 MB, cached for next time). Parsing takes a few seconds for typical documents and up to a minute for very large PDFs.
  3. 3

    Switch between Text, JSON, and Pages tabs

    The Text tab shows layout-preserved plain text — paragraphs and columns are kept intact. The JSON tab lists every text span with its bounding box. The Pages tab shows a rendered preview of each page.
  4. 4

    Copy or download the output

    Click Copy to put the active tab's content on your clipboard, or Download to save it as a .txt, .json, or .png file. Use this output in your own pipeline — search indexing, RAG, data extraction, archival.
  5. 5

    Parse another PDF

    Hit the Parse another button or just drop a new file. The WASM engine stays loaded so subsequent parses start instantly.

Common use cases

1

Confidential documents

Contracts, payslips, medical records, internal reports — anything you wouldn't want crossing the public internet. Because the parser runs in your browser, the bytes stay on your device.
2

RAG / LLM pipelines

Extract clean text from a research paper or report before embedding it for retrieval-augmented generation. The JSON output also gives you per-page bounding boxes for citation rendering.
3

Data extraction from forms

Pull values from a structured PDF form. The JSON output preserves the position of each text span, so you can map fields to your schema even when labels move around.
4

Quick text grep

Sometimes you just want to copy the text out of a PDF to paste somewhere else. Drop, copy, done — no roundtrips, no sign-in, no ads.

About This Tool

This tool extracts text, layout, and per-span bounding-box coordinates from PDFs entirely inside your browser. The parsing engine is liteparse, a Rust-based PDF parser compiled to WebAssembly and shipped to your tab over a normal static asset request. Once loaded, it processes the bytes from your file without ever sending them anywhere.

Three output flavours fall out of a single parse: layout-preserved plain text suitable for copy-paste or feeding into a downstream pipeline; structured JSON with every text span carrying its bounding box and page number; and rendered PNG previews of each page. The JSON view is the most useful for RAG / LLM pipelines, citation rendering, and structured data extraction. The text view is the fastest path to getting copy out of a PDF and into a document.

Privacy is the headline feature. Every competing online PDF parser uploads your file to a server. For sensitive documents — contracts, payslips, medical records, internal memos — that's a real risk. Open your browser's Network tab while parsing here and you'll see zero outbound requests carrying your data. The PDF bytes stay in this tab and are dropped from memory the moment you refresh or close.

How It Compares

Unlike server-backed tools such as smallpdf.com or ilovepdf.com, this PDF text extractor processes everything locally via WebAssembly. There is no upload, no progress bar waiting for a round-trip, no terms-of-service warning about uploaded files. You also get richer output than text-only competitors: structured JSON with bounding boxes plus rendered page previews. The WASM bundle is around 4 MB — cached by your browser after first use, so subsequent parses start instantly.

PDF extraction tips

1
If the Text tab is empty but the Pages tab shows your content, the PDF is image-based (scanned). You'll need an OCR tool to recover the text.
2
The JSON output's bounding-box coordinates are in PDF page units — usually 1 unit = 1/72 inch. Multiply by your render DPI to convert to pixels.
3
For multi-column PDFs, the layout-preserved text mode reads the columns in order. If you want raw reading order instead, fall back to the JSON output and sort spans by Y then X yourself.
4
Encrypted PDFs need to be decrypted before parsing. Most desktop PDF tools (Preview on Mac, Adobe Reader, qpdf) can remove the password if you have it.
5
The WASM bundle is cached by the browser after first load, so the second and later parses start almost instantly. Refresh and you should notice the difference.

Frequently Asked Questions

1

Is my PDF uploaded to a server?

No. The entire parser runs inside your browser as WebAssembly. Open the Network tab in your developer tools while you parse — you'll see zero outbound requests for your file. The PDF bytes never leave the tab.
2

What output formats are supported?

Three outputs from a single parse: layout-preserved plain text, structured JSON with every text span's bounding box and page number, and rendered PNG previews of each page. You can copy any tab to clipboard or download as a file.
3

How large of a PDF can I parse?

The tool caps uploads at 50 MB so memory stays bounded across browsers. For larger files, split the PDF first and parse the pieces separately. Most modern PDFs under 200 pages fit comfortably under the cap.
4

Does this work for scanned (image-based) PDFs?

Partially. Native text in a PDF is extracted directly. Scanned pages would need OCR, which is not bundled here to keep the download small. If your text comes back empty, the PDF is likely a scan — try an OCR tool first.
5

Can I parse password-protected PDFs?

Not yet from this UI. Liteparse supports a password option in its API, but we haven't exposed a password field to keep the interface simple. Remove the password first using a PDF tool, then drop the unprotected file here.

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