@tom_doerr: Converts images and PDFs to Markdown without OCR https://github.com/NanoNets/docext

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document-extraction pdf-to-markdown vision-language-model ocr-free open-source nanonets

Summary

docext is an on-premises toolkit that converts images and PDFs to markdown without OCR, leveraging vision-language models. It also introduces Nanonets-OCR-s, a compact 3B parameter model for efficient image-to-markdown conversion.

Converts images and PDFs to Markdown without OCR https://t.co/tI0EDdaWZR https://t.co/eFGzn4DLRa
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Converts images and PDFs to Markdown without OCR

https://t.co/tI0EDdaWZR https://t.co/eFGzn4DLRa

NanoNets/docext

Source: https://github.com/NanoNets/docext

docext

An on-premises document information extraction and benchmarking toolkit.

PyPI Downloads License Open In Colab PyPI - Version

Demo Docext

New Model Release: Nanonets-OCR-s

We’re excited to announce the release of Nanonets-OCR-s, a compact 3B parameter model specifically trained for efficient image to markdown conversion with semantic understanding for images, signatures, watermarks, etc.!

📢 Read the full announcement | 🤗 Hugging Face model

Overview

docext is a comprehensive on-premises document intelligence toolkit powered by vision-language models (VLMs). It provides three core capabilities:

📄 PDF & Image to Markdown Conversion: Transform documents into structured markdown with intelligent content recognition, including LaTeX equations, signatures, watermarks, tables, and semantic tagging.

🔍 Document Information Extraction: OCR-free extraction of structured information (fields, tables, etc.) from documents such as invoices, passports, and other document types, with confidence scoring.

📊 Intelligent Document Processing Leaderboard: A comprehensive benchmarking platform that tracks and evaluates vision-language model performance across OCR, Key Information Extraction (KIE), document classification, table extraction, and other intelligent document processing tasks.

Features

PDF and Image to Markdown

Convert both PDF and images to markdown with content recognition and semantic tagging.

  • LaTeX Equation Recognition: Convert both inline and block LaTeX equations in images to markdown.
  • Intelligent Image Description: Generate a detailed description for all images in the document within <img></img> tags.
  • Signature Detection: Detect and mark signatures and watermarks in the document. Signatures text are extracted within <signature></signature> tags.
  • Watermark Detection: Detect and mark watermarks in the document. Watermarks text are extracted within <watermark></watermark> tags.
  • Page Number Detection: Detect and mark page numbers in the document. Page numbers are extracted within <page_number></page_number> tags.
  • Checkboxes and Radio Buttons: Converts form checkboxes and radio buttons into standardized Unicode symbols (☐, ☑, ☒).
  • Table Detection: Convert complex tables into html tables.

🔍 For in-depth information, see the release blog.

For setup instructions and additional details, check out the full feature guide for the pdf to markdown.

Intelligent Document Processing Leaderboard

This benchmark evaluates performance across seven key document intelligence challenges:

  • Key Information Extraction (KIE): Extract structured fields from unstructured document text.
  • Visual Question Answering (VQA): Assess understanding of document content via question-answering.
  • Optical Character Recognition (OCR): Measure accuracy in recognizing printed and handwritten text.
  • Document Classification: Evaluate how accurately models categorize various document types.
  • Long Document Processing: Test models’ reasoning over lengthy, context-rich documents.
  • Table Extraction: Benchmark structured data extraction from complex tabular formats.
  • Confidence Score Calibration: Evaluate the reliability and confidence of model predictions.

🔍 For in-depth information, see the release blog.

📊 Live leaderboard: https://idp-leaderboard.org

For setup instructions and additional details, check out the full feature guide for the Intelligent Document Processing Leaderboard.

Docext

  • Flexible extraction: Define custom fields or use pre-built templates
  • Table extraction: Extract structured tabular data from documents
  • Confidence scoring: Get confidence levels for extracted information
  • On-premises deployment: Run entirely on your own infrastructure (Linux, MacOS)
  • Multi-page support: Process documents with multiple pages
  • REST API: Programmatic access for integration with your applications
  • Pre-built templates: Ready-to-use templates for common document types:
    • Invoices
    • Passports
    • Add/delete new fields/columns for other templates.

For more details (Installation, Usage, and so on), please check out the feature guide.

Change Log

Latest Updates

  • 12-06-2025 - Added pdf and image to markdown support.
  • 06-06-2025 - Added gemini-2.5-pro-preview-06-05 evaluation metrics to the leaderboard.
  • 04-06-2025 - Added support for PDF and multiple documents in docext extraction.
Older Changes
  • 23-05-2025 – Added gemini-2.5-pro-preview-03-25, claude-sonnet-4 evaluation metrics to the leaderboard.
  • 17-05-2025 – Added InternVL3-38B-Instruct, qwen2.5-vl-32b-instruct evaluation metrics to the leaderboard.
  • 16-05-2025 – Added gemma-3-27b-it evaluation metrics to the leaderboard.
  • 12-05-2025 – Added Claude 3.7 sonnet, mistral-medium-3 evaluation metrics to the leaderboard.

About

docext is developed by Nanonets, a leader in document AI and intelligent document processing solutions. Nanonets is committed to advancing the field of document understanding through open-source contributions and innovative AI technologies. If you are looking for information extraction solutions for your business, please visit our website to learn more.

Contributing

We welcome contributions! Please see contribution.md for guidelines. If you have a feature request or need support for a new model, feel free to open an issue—we’d love to discuss it further!

Troubleshooting

If you encounter any issues while using docext, please refer to our Troubleshooting guide for common problems and solutions.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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