One-click tool that cleans, OCRs, extracts text and metadata, and exports your PDF as LLM-ready Markdown or JSON — perfect input for GPT, Claude, Gemini, vector indexes and any retrieval-augmented generation pipeline.
Click to select a file or drag and drop
One or more PDF files
Your files never leave your device.
Output Format:
Each PDF will be extracted as a JSON file containing an array of LlamaIndex Document objects with:
text -
Extracted text content per page
metadata -
Page number, headings, and document info
extra_info -
Additional context for RAG systems
Processing...
Drag a single PDF onto the upload zone or pick it from your device. The file is processed locally in your browser — nothing is uploaded.
ZentoPDF runs OCR if the PDF is scanned, removes headers/footers and watermarks, normalises whitespace and rebuilds the document into clean Markdown plus a structured JSON sidecar with chunk metadata.
Grab the Markdown for direct prompting and the JSON for RAG indexing. Each chunk carries its page number, heading path and approximate token count — ready to feed straight into your LLM pipeline.
Make a PDF searchable and copyable.
Extract text from PDF files and save as plain text (.txt). Supports multiple files.
Convert PDF text and tables to Markdown format.
Extract tables from PDF files and export as CSV, JSON, or Markdown.
Reduce the file size of your PDF documents while maintaining the best possible quality.
Raw PDFs are awful inputs for LLMs: they contain bytes-level fonts, columns out of reading order, headers/footers repeated on every page, scanned pages with no real text, and no chunk boundaries. Preparing a PDF turns it into clean, structured text + metadata your model can actually read and your vector index can store sensibly.
Two: a Markdown (.md) file with proper headings, paragraphs and lists, ideal for direct LLM prompts; and a JSON sidecar with one entry per chunk (text, page, heading path, approximate token count) — perfect for vector embedding and retrieval-augmented generation.
Yes. If a page has no selectable text, ZentoPDF runs Tesseract.js OCR in your browser to recognise it before extraction. The OCR layer is also embedded into the Markdown so your AI sees real words instead of an empty page.
No. The entire pipeline runs in your browser with PDF.js, Tesseract.js and our text processors. Your PDF and the generated AI-ready files never leave your device, which makes the tool safe for proprietary research, legal and internal documents.
Yes — Markdown is the universal LLM-friendly format. You can paste it directly into any chat UI, or stream the chunked JSON into LangChain, LlamaIndex, LiteLLM or your own RAG pipeline without any further normalisation step.