PDF Text Extractor
Extract text, Markdown, JSON with bounding boxes, or CSV from a PDF. On this page, liteparse WebAssembly parses it in your browser; the file is not uploaded.
- Free, no sign-up
- REST + MCP
- Updated
- Reviewed by Olgun Ozoktas
How to extract text from a PDF in your browser
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Drop or select your PDF
Drag a PDF file into the upload zone, or click it to open the file picker. On this page, the file is loaded into your browser's memory and is not uploaded. -
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. -
Switch between the Text, Markdown, JSON, and Pages tabs
The Text tab shows layout-preserved plain text, with paragraphs and columns kept intact. The Markdown tab turns that text into headings, lists and paragraphs. The JSON tab lists every text span with its bounding box. The Pages tab shows each page with its own downloads. -
Copy or download the output
Click Copy to put the active tab's content on your clipboard, or download it as a .txt, .md, .html, .json or .csv file. Use this output in your own pipeline — search indexing, RAG, data extraction, archival. -
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
Confidential documents
RAG / LLM pipelines
Data extraction from forms
Quick text grep
About this tool
This tool extracts text, layout, and per-span bounding-box coordinates from PDFs inside your browser on this page. 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.
One parse gives four views: layout-preserved plain text suitable for copy-paste or feeding into a downstream pipeline; Markdown with headings and lists detected from that text; structured JSON with every text span carrying its bounding box and page number; and a Pages tab with one card per page. Downloads are .txt, .md, .html, .json and .csv for the whole document or a single page, plus a .png of a page when the parser returns page images. 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. Many online PDF parsers upload 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. The REST API and MCP tool are a separate path: a call sends the input you include to FindUtils over HTTPS, where it is processed and the result returned. FindUtils does not store that input, and logs the request without its content.
How it compares
PDF extraction tips
- 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.
- 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.
- 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.
- Encrypted PDFs need to be decrypted before parsing. If you know the password, remove it with Unlock PDF on FindUtils first.
- 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.