# docext

**Repository Path**: fork-gitee/docext

## Basic Information

- **Project Name**: docext
- **Description**: docext 是用于从文档提取非结构化信息的本地化开源工具，无需 OCR，利用视觉语言模型(VLM)来识别和提取文档中的字段数据和表格信息，既准确又能保证数据安全隐私
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: dev/benchmark
- **Homepage**: https://www.oschina.net/p/docext
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2025-06-05
- **Last Updated**: 2025-06-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<h1 align="center">docext</h1>


<p align="center"><em>An on-premises document information extraction and benchmarking toolkit.</em></p>

<p align="center">
  <a href="https://pepy.tech/projects/docext">
    <img src="https://static.pepy.tech/badge/docext" alt="PyPI Downloads" />
  </a>
  <a href="https://opensource.org/licenses/Apache-2.0">
    <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License" />
  </a>
  <a href="https://colab.research.google.com/drive/1r1asxGeezfWnJvw8jimfFAB2sGjk1HdM?usp=sharing">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" />
  </a>
  <a href="https://pypi.org/project/docext/">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/docext">
  </a>
</p>

<p align="center">
  <img src="https://raw.githubusercontent.com/NanoNets/docext/main/assets/demo.png" style="max-width: 45%; height: auto;" />
  <img src="assets/benchmark.png" style="max-width: 45%; height: auto;" />
</p>



## Overview

docext is an OCR-free tool for extracting structured information from documents such as invoices, passports, and other documents. It leverages vision-language models (VLMs) to accurately identify and extract both field data and tabular information from document images.

The [Intelligent Document Processing Leaderboard](https://idp-leaderboard.org/) tracks and evaluates performance vision-language models across OCR, Key Information Extraction (KIE), document classification, table extraction, and other intelligent document processing tasks.


## Features
### 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](https://github.com/NanoNets/docext/tree/main/docext/benchmark).

📊 **Live leaderboard:** [https://idp-leaderboard.org](https://idp-leaderboard.org)

For setup instructions and additional details, check out the full feature guide for the [Intelligent Document Processing Leaderboard](https://github.com/NanoNets/docext/tree/main/docext/benchmark).

### 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 on the features, please check out the [feature guide](https://github.com/NanoNets/docext/tree/main/docext/benchmark).

## About

docext is developed by [Nanonets](https://nanonets.com), 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](https://nanonets.com) to learn more.

## Contributing

We welcome contributions! Please see [contribution.md](https://github.com/NanoNets/docext/blob/main/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](https://github.com/NanoNets/docext/blob/main/Troubleshooting.md) for common problems and solutions.


## License

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