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# <img src="https://public-vlms.s3.us-west-2.amazonaws.com/docstrange_logo.svg" alt="DocStrange" width="32" style="vertical-align: middle; margin-right: 8px;">  DocStrange

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> 🚀 **[Try DocStrange Online →](https://docstrange.nanonets.com/)**

# DocStrange

DocStrange converts documents to Markdown, JSON, CSV, and HTML quickly and accurately.

- Converts PDF, image, PPTX, DOCX, XLSX, and URL files.
- Formats tables into clean, LLM-optimized Markdown.
- Powered by an upgraded 7B model for higher accuracy and deeper document understanding.
- Extracts text from images and scanned documents with advanced OCR.
- Removes page artifacts for clean, readable output.
- Does structured extraction, given specific fields or a JSON schema.
- Includes a built-in, local Web UI for easy drag-and-drop conversion.
- Offers a free cloud API for instant processing or a 100% private, local mode.
- Works on GPU or CPU when running locally.
- Integrates with Claude Desktop via an MCP server for intelligent document navigation.

---

![DocStrange Demo](https://public-vlms.s3.us-west-2.amazonaws.com/markdown.gif)


## Processing Modes
> **☁️ Free Cloud Processing upto 10000 docs per month !**  
> Extract documents data instantly with the cloud processing - no complex setup needed 

> **🔒 Local Processing !**  
> Use `gpu` mode for 100% local processing - no data sent anywhere, everything stays on your machine.


## **What's New**

**August 2025**

- 🚀 **Major Model Upgrade**: The core model has been upgraded to **7B parameters**, delivering significantly higher accuracy and deeper understanding of complex documents.
- 🖥️ **Local Web Interface**: Introducing a built-in, local GUI. Now you can convert documents with a simple drag-and-drop interface, 100% offline.

---

## About

Convert and extract data from PDF, DOCX, images, and more into clean Markdown and structured JSON. Plus: Advanced table extraction, 100% local processing, and a built-in web UI.

`DocStrange` is a Python library for converting a wide range of document formats—including **PDF**, **DOCX**, **PPTX**, **XLSX**, and **images** — into clean, usable data. It produces LLM-optimized **Markdown**, structured **JSON** (with schema support), **HTML**, and **CSV** outputs, making it an ideal tool for preparing content for RAG pipelines and other AI applications.

The library offers both a powerful cloud API and a 100% private, offline mode that runs locally on your GPU. Developed by **Nanonets**, DocStrange is built on a powerful pipeline of OCR and layout detection models and currently requires **Python >=3.8**.

**To report a bug or request a feature, [please file an issue](https://github.com/NanoNets/docstrange/issues). To ask a question or request assistance, please use the [discussions forum](https://github.com/NanoNets/docstrange/discussions).**

---

## **How DocStrange Differs**

`DocStrange` focuses on end-to-end document understanding (OCR → layout → tables → clean Markdown or structured JSON) that you can run 100% locally. It is designed to deliver high-quality results from scans and photos without requiring the integration of multiple services.

- **vs. Cloud AI Services (like AWS Textract)**: `DocStrange` offers a completely private, local processing option and gives you full control over the conversion pipeline.
- **vs. Orchestration Frameworks (like LangChain)**: `DocStrange` is a ready-to-use parsing pipeline, not just a framework. It handles the complex OCR and layout analysis so you don't have to build it yourself.
- **vs. Other Document Parsers**: `DocStrange` is specifically built for robust OCR on scans and phone photos, not just digitally-native PDFs.

### **When to Pick DocStrange**
- You need a **free cloud api** to extract information in structured format (markdown, json, csv, html) from different document types
- You need **local processing** for privacy and compliance.
- You are working with **scans, phone photos, or receipts** where high-quality OCR is critical.
- You need a **fast path to clean Markdown or structured JSON** without training a model.

---

## **Examples**

Try the live demo: Test `DocStrange` instantly in your browser with no installation required at [docstrange.nanonets.com](https://docstrange.nanonets.com/)

**See it in action:**

![DocStrange Demo](https://public-vlms.s3.us-west-2.amazonaws.com/docstrange.gif) 

<!-- 
**Example outputs: Here's a quick preview of the quality of output**

| Document Type | Source File | Output (Markdown) | Output (JSON) | Output (CSV) |
| --- | --- | --- | --- | --- |
| **Invoice PDF** | invoice.pdf | View Markdown | View JSON | View CSV |
| **Research Paper** | paper.pdf | View Markdown | View JSON | NA |
| **Word Document** | report.docx | View Markdown | View JSON | NA |
| **Scanned Invoice** | [Ziebart.JPG](https://nanonets.com/media/1587320232578_ziebart.jpeg) | View Markdown | View JSON | View CSV | -->

---

## **Installation**
Install the library using pip:

```bash
pip install docstrange
```

## **Quick Start**

> 💡 **New to DocStrange?** Try the [online demo](https://docstrange.nanonets.com/) first - no installation needed!

**1. Convert any Document to LLM-Ready Markdown**

This is the most common use case. Turn a complex PDF or DOCX file into clean, structured Markdown, perfect for RAG pipelines and other LLM applications.

```python
from docstrange import DocumentExtractor

# Initialize extractor (cloud mode by default)
extractor = DocumentExtractor()

# Convert any document to clean markdown
result = extractor.extract("document.pdf")
markdown = result.extract_markdown()
print(markdown)
```

**2. Extract Structured Data as JSON**

Go beyond plain text and extract all detected entities and content from your document into a structured JSON format.

```python
from docstrange import DocumentExtractor

# Extract document as structured JSON
extractor = DocumentExtractor()
result = extractor.extract("document.pdf")

# Get all important data as flat JSON
json_data = result.extract_data()
print(json_data)
```

**3. Extract Specific Fields from a PDF or Invoice** 

Target only the key-value data you need, such as extracting the invoice_number or total_amount directly from a document.

```python
from docstrange import DocumentExtractor

# Extract only the fields you need
extractor = DocumentExtractor()
result = extractor.extract("invoice.pdf")

# Specify exactly which fields to extract
fields = result.extract_data(specified_fields=[
    "invoice_number", "total_amount", "vendor_name", "due_date"
])
print(fields)
```

**4. Extract with Custom JSON Schema**

Ensure the structure of your output by providing a custom JSON schema. This is ideal for getting reliable, nested data structures for applications that process contracts or complex forms.

```python
from docstrange import DocumentExtractor

# Extract data conforming to your schema
extractor = DocumentExtractor()
result = extractor.extract("contract.pdf")

# Define your required structure
schema = {
    "contract_number": "string",
    "parties": ["string"],
    "total_value": "number",
    "start_date": "string",
    "terms": ["string"]
}

structured_data = result.extract_data(json_schema=schema)
print(structured_data)
```

**Local Processing**

For complete privacy and offline capability, run DocStrange entirely on your own machine using GPU processing.

```python
# Force local GPU processing (requires CUDA)
extractor = DocumentExtractor(gpu=True)
```

---

## Local Web Interface

💡 Want a GUI? Run the simple, drag-and-drop local web interface for private, offline document conversion.

For users who prefer a graphical interface, DocStrange includes a powerful, self-hosted web UI. This allows for easy drag-and-drop conversion of PDF, DOCX, and other files directly in your browser, with 100% private, offline processing on your own GPU. The interface automatically downloads required models on its first run.

### How to get started?

1. **Install with web dependencies:**

```bash
pip install "docstrange[web]"
```

2. **Run the web interface:**

```bash
# Method 1: Using the CLI command
docstrange web

# Method 2: Using Python module
python -m docstrange.web_app

# Method 3: Direct Python import
python -c "from docstrange.web_app import run_web_app; run_web_app()"
```

3. **Open your browser:** Navigate to `http://localhost:8000` (or the port shown in the terminal)

### **Features of DocStrange's Local Web Interface:**

- 🖱️ Drag & Drop Interface: Simply drag files onto the upload area.
- 📁 Multiple File Types: Supports PDF, DOCX, XLSX, PPTX, images, and more.
- ⚙️ Processing Modes: Choose between Cloud and Local GPU processing.
- 📊 Multiple Output Formats: Get Markdown, HTML, JSON, CSV, and Flat JSON.
- 🔒 Privacy Options: Choose between cloud processing (default) or local GPU processing.
- 📱 Responsive Design: Works on desktop, tablet, and mobile

### **Supported File Types:**

- **Documents**: PDF, DOCX, DOC, PPTX, PPT
- **Spreadsheets**: XLSX, XLS, CSV
- **Images**: PNG, JPG, JPEG, TIFF, BMP
- **Web**: HTML, HTM
- **Text**: TXT

### **Processing Modes:**

- **Cloud processing:** For instant, zero-setup conversion, you can head over to [docstrange.nanonets.com](http://docstrange.nanonets.com/) **—** no setup (default)
- **Local GPU**: Fastest local processing, requires CUDA support

### **Output Formats:**

- **Markdown**: Clean, structured text perfect for documentation
- **HTML**: Formatted output with styling and layout
- **CSV**: Table data in spreadsheet format
- **Flat JSON**: Simplified JSON structure
- **Specific Fields**: Specific information from documents


### **Advanced Usage:**

1. Run on a Custom Port:

```bash
# Run on a different port
docstrange web --port 8080
python -c "from docstrange.web_app import run_web_app; run_web_app(port=8080)"
```

2. Run in Development Mode:

```bash
# Run with debug mode for development
python -c "from docstrange.web_app import run_web_app; run_web_app(debug=True)"
```

3. Run on a Custom Host (to make it accessible on your local network):

```bash
# Make accessible from other devices on the network
python -c "from docstrange.web_app import run_web_app; run_web_app(host='0.0.0.0')"
```

### **Troubleshooting**

1. Port Already in Use:

```bash
# Use a different port
docstrange web --port 8001
```

2. GPU Not Available:

- The interface automatically detects GPU availability
- GPU option will be disabled if CUDA is not available
- Error will be thrown

3. Model Download Issues:

- Models are downloaded automatically on first startup
- Check your internet connection during initial setup
- Download progress is shown in the terminal

4. Installation Issues:

```bash
# Install with all dependencies
pip install -e ".[web]"
# Or install Flask separately
pip install Flask
```

**Cloud Alternative**

Need cloud processing? Use the official DocStrange Cloud service: 🔗 **[docstrange.nanonets.com](https://docstrange.nanonets.com/)**

---

## Usage and Features

You can use DocStrange in three main ways: as a simple Web Interface, as a flexible Python Library, or as a powerful Command Line Interface (CLI). This section provides a summary of the library's key capabilities, followed by detailed guides and examples for each method.

1. **Convert Multiple File Types**

DocStrange natively handles a wide variety of formats, returning the most appropriate output for each.

```python
from docstrange import DocumentExtractor

extractor = DocumentExtractor()

# PDF document
pdf_result = extractor.extract("report.pdf")
print(pdf_result.extract_markdown())

# Word document
docx_result = extractor.extract("document.docx")
print(docx_result.extract_data())

# Excel spreadsheet
excel_result = extractor.extract("data.xlsx")
print(excel_result.extract_csv())

# PowerPoint presentation
pptx_result = extractor.extract("slides.pptx")
print(pptx_result.extract_html())

# Image with text
image_result = extractor.extract("screenshot.png")
print(image_result.extract_text())

# Web page
url_result = extractor.extract("https://example.com")
print(url_result.extract_markdown())
```

**b. Extract Tables to CSV**

Easily extracts all tables from a document into a clean CSV format.

```python
# Extract all tables from a document
result = extractor.extract("financial_report.pdf")
csv_data = result.extract_csv()
print(csv_data)
```


**c. Extract Specific Fields & Structured Data**

You can go beyond simple conversion and extract data in the exact structure you require. There are two ways to do this. You can either target and pull only the key-value data you need or ensure the structure of your output by providing a custom JSON schema. 

```python
# Extract specific fields from any document
result = extractor.extract("invoice.pdf")

# Method 1: Extract specific fields
extracted = result.extract_data(specified_fields=[
    "invoice_number",
    "total_amount", 
    "vendor_name",
    "due_date"
])

# Method 2: Extract using JSON schema
schema = {
    "invoice_number": "string",
    "total_amount": "number", 
    "vendor_name": "string",
    "line_items": [{
        "description": "string",
        "amount": "number"
    }]
}

structured = result.extract_data(json_schema=schema)
```

**d. Cloud Mode Usage Examples:**

Use DocStrange's cloud mode to extract precise, structured data from various documents by either specifying a list of fields to find or enforcing a custom JSON schema for the output. Authenticate with DocStrange login or a free API key to get 10,000 documents/month.

```python
from docstrange import DocumentExtractor

# Default cloud mode (rate-limited without API key)
extractor = DocumentExtractor()

# Authenticated mode (10k docs/month) - run 'docstrange login' first
extractor = DocumentExtractor()  # Auto-uses cached credentials

# With API key for 10k docs/month (alternative to login)
extractor = DocumentExtractor(api_key="your_api_key_here")

# Extract specific fields from invoice
result = extractor.extract("invoice.pdf")

# Extract key invoice information
invoice_fields = result.extract_data(specified_fields=[
    "invoice_number",
    "total_amount", 
    "vendor_name",
    "due_date",
    "items_count"
])

print("Extracted Invoice Fields:")
print(invoice_fields)
# Output: {"extracted_fields": {"invoice_number": "INV-001", ...}, "format": "specified_fields"}

# Extract structured data using schema
invoice_schema = {
    "invoice_number": "string",
    "total_amount": "number",
    "vendor_name": "string",
    "billing_address": {
        "street": "string",
        "city": "string", 
        "zip_code": "string"
    },
    "line_items": [{
        "description": "string",
        "quantity": "number",
        "unit_price": "number",
        "total": "number"
    }],
    "taxes": {
        "tax_rate": "number",
        "tax_amount": "number"
    }
}

structured_invoice = result.extract_data(json_schema=invoice_schema)
print("Structured Invoice Data:")
print(structured_invoice)
# Output: {"structured_data": {...}, "schema": {...}, "format": "structured_json"}

# Extract from different document types
receipt = extractor.extract("receipt.jpg")
receipt_data = receipt.extract_data(specified_fields=[
    "merchant_name", "total_amount", "date", "payment_method"
])

contract = extractor.extract("contract.pdf") 
contract_schema = {
    "parties": [{
        "name": "string",
        "role": "string"
    }],
    "contract_value": "number",
    "start_date": "string",
    "end_date": "string",
    "key_terms": ["string"]
}
contract_data = contract.extract_data(json_schema=contract_schema)
```


**e. Chain with LLM**

The clean Markdown output is perfect for use in Retrieval-Augmented Generation (RAG) and other LLM workflows. 

```python
# Perfect for LLM workflows
document_text = extractor.extract("research_paper.pdf").extract_markdown()

# Use with any LLM
response = your_llm_client.chat(
    messages=[{
        "role": "user", 
        "content": f"Summarize this research paper:\n\n{document_text}"
    }]
)
```

### **Key Capabilities**

- **🌐 Universal Input**: Process a wide range of formats, including **PDF**, **DOCX**, **PPTX**, **XLSX**, images, and URLs.
- **🔒 Dual Processing Modes**: Choose between a cloud API for instant processing or **100% private, local processing** on your own CPU or GPU.
- **🤖 Intelligent Extraction**: Extract **specific fields** or enforce a nested **JSON schema** to get structured data output.
- **🖼️ Advanced OCR**: Handle scanned documents and images with an OCR pipeline that includes **multiple engine fallbacks**.
- **📊 Table & Structure Recognition**: Accurately **extract tables** and preserve document structure, producing clean, **LLM-optimized** output.
- **🖥️ Built-in Web UI**: Use the built-in **drag-and-drop web interface** for easy local conversions.

### **How It Works**

DocStrange uses a multi-stage process to create structured output from documents.

1. **Ingestion**: It natively handles various file formats, including PDF, DOCX, PPTX, images, and URLs.
2. **Layout Detection**: The library identifies the structure of the document, such as headers, paragraphs, lists, and tables, to preserve the original reading order.
3. **OCR & Text Extraction**: It employs advanced OCR for scanned documents and directly extracts text from digital files.
4. **Formatting & Cleaning**: The extracted content is converted into clean, LLM-optimized Markdown and other formats, removing page artifacts.
5. **Structured Extraction (Optional)**: If a schema or specific fields are provided, DocStrange uses an LLM to populate the desired JSON structure.

---

## Cloud API Tiers and Rate Limits

`DocStrange` offers free cloud processing with different tiers to ensure fair usage.

- **🔐 Authenticated Access (Recommended)**
    - **Rate Limit**: **10,000 documents/month**.
    - **Setup**: A single command: `docstrange login`.
    - **Benefits**: Links to your Google account for a significantly higher free limit.
- **🔑 API Key Access (Alternative)**
    - **Rate Limit**: **10,000 documents/month**.
    - **Setup**: Get a free API key from [docstrange.nanonets.com](https://docstrange.nanonets.com/).
    - Usage: Pass the API key when initializing the library.

```python
# Free tier usage (limited calls daily)
extractor = DocumentExtractor()

# Authenticated access (10k docs/month) - run 'docstrange login' first
extractor = DocumentExtractor()  # Auto-uses cached credentials

# API key access (10k docs/month)
extractor = DocumentExtractor(api_key="your_api_key_here")
```

💡 **Tip**: Start with the anonymous free tier to test functionality, then authenticate with `docstrange login` for the full 10,000 documents/month limit.

---

## **Command Line Interface (CLI)**

💡 **Prefer a GUI?** Try the [web interface](https://docstrange.nanonets.com/) for drag-and-drop document conversion!

For automation, scripting, and batch processing, you can use DocStrange directly from your terminal.

**Authentication Commands**

```bash
# One-time login for free 10k docs/month (alternative to api key)
docstrange login

# Alternatively
docstrange --login

# Re-authenticate if needed
docstrange login --reauth

# Logout and clear cached credentials
docstrange --logout
```

**Document Processing**

```bash
# Basic conversion (cloud mode default - limited calls free!)
docstrange document.pdf

# Authenticated processing (10k docs/month for free after login)
docstrange document.pdf

# With API key for 10k docs/month access (alternative to login)
docstrange document.pdf --api-key YOUR_API_KEY

# Local processing modes
docstrange document.pdf --gpu-mode

# Different output formats
docstrange document.pdf --output json
docstrange document.pdf --output html
docstrange document.pdf --output csv

# Extract specific fields
docstrange invoice.pdf --output json --extract-fields invoice_number total_amount

# Extract with JSON schema
docstrange document.pdf --output json --json-schema schema.json

# Multiple files
docstrange *.pdf --output markdown

# Save to file
docstrange document.pdf --output-file result.md

# Comprehensive field extraction examples
docstrange invoice.pdf --output json --extract-fields invoice_number vendor_name total_amount due_date line_items

# Extract from different document types with specific fields
docstrange receipt.jpg --output json --extract-fields merchant_name total_amount date payment_method

docstrange contract.pdf --output json --extract-fields parties contract_value start_date end_date

# Using JSON schema files for structured extraction
docstrange invoice.pdf --output json --json-schema invoice_schema.json
docstrange contract.pdf --output json --json-schema contract_schema.json

# Combine with authentication for 10k docs/month access (after 'docstrange login')
docstrange document.pdf --output json --extract-fields title author date summary

# Or use API key for 10k docs/month access (alternative to login)
docstrange document.pdf --api-key YOUR_API_KEY --output json --extract-fields title author date summary

```

**Example schema.json file:**

```json
{
  "invoice_number": "string",
  "total_amount": "number",
  "vendor_name": "string",
  "billing_address": {
    "street": "string",
    "city": "string",
    "zip_code": "string"
  },
  "line_items": [{
    "description": "string",
    "quantity": "number",
    "unit_price": "number"
  }]
}
```

## **API Reference for library**

This section details the main classes and methods for programmatic use. 

1. **DocumentExtractor**

```python
DocumentExtractor(
    api_key: str = None,              # API key for 10k docs/month (or use 'docstrange login' for same limits)
    model: str = None,                # Model for cloud processing ("gemini", "openapi", "nanonets")
    cpu: bool = False,                # Force local CPU processing
    gpu: bool = False                 # Force local GPU processing
)
```

**b. ConversionResult Methods**

```python
result.extract_markdown() -> str                    # Clean markdown output
result.extract_data(                              # Structured JSON
    specified_fields: List[str] = None,       # Extract specific fields
    json_schema: Dict = None                  # Extract with schema
) -> Dict
result.extract_html() -> str                      # Formatted HTML
result.extract_csv() -> str                       # CSV format for tables
result.extract_text() -> str                      # Plain text
```

---

## **🤖 MCP Server for Claude Desktop (Local Development)**

The DocStrange repository includes an optional MCP (Model Context Protocol) server for local development that enables intelligent document processing in Claude Desktop with token-aware navigation.

> Note: The MCP server is designed for local development and is **not included** in the PyPI package. Clone the repository to use it locally.

**Features**

- **Smart Token Counting**: Automatically counts tokens and recommends processing strategy
- **Hierarchical Navigation**: Navigate documents by structure when they exceed context limits
- **Intelligent Chunking**: Automatically splits large documents into token-limited chunks
- **Advanced Search**: Search within documents and get contextual results

**Local Setup**

1. Clone the repository:

```bash
git clone https://github.com/nanonets/docstrange.git
cd docstrange
```

2. Install in development mode:

```bash
pip install -e ".[dev]"
```

3. Add to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "docstrange": {
      "command": "python3",
      "args": ["/path/to/docstrange/mcp_server_module/server.py"]
    }
  }
}
```

4. Restart Claude Desktop

For detailed setup and usage, see [mcp_server_module/README.md](https://github.com/NanoNets/docstrange/blob/main/mcp_server_module/README.md)

---


## **The Nanonets Ecosystem**

`DocStrange` is a powerful open-source library developed and maintained by the team at **Nanonets**. The full Nanonets platform is an AI-driven solution for automating end-to-end document processing for businesses. The platform allows technical and non-technical teams to build complete automated document workflows.

## **Community, Support, & License**

This is an actively developed open-source project, and we welcome your feedback and contributions.

- **Discussions**: For questions, ideas, and to show what you've built, please visit our [**GitHub Discussions**](https://www.google.com/search?q=URL_TO_GITHUB_DIScussions).
- **Issues**: For bug reports and feature requests, please open an [**Issue**](https://www.google.com/search?q=URL_TO_GITHUB_ISSUES).
- **Email**: For private inquiries, you can reach us at [**support@nanonets.com**](mailto:support@nanonets.com).

⭐ Star this repo if you find it helpful! Your support helps us improve the library.

**License:** This project is licensed under the **MIT License.** 
