A Python CLI tool to extract text and structured data from PDF files using Azure's Document Intelligence service (formerly Form Recognizer).

• 📄 Extract plain text from PDF files
• 🌐 Web Interface - User-friendly web UI for easy uploading and extraction
• 📝 Extract and format as Markdown - Get beautifully formatted markdown with headings, tables, and proper structure
• 📊 Extract structured data (tables, key-value pairs, paragraphs)
• 💾 Save extracted content to files
• 🔐 Secure credential management using .env files
• 🎯 Simple command-line interface

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

pip install -r requirements.txt

Copy your .env file from your other project or create a new one:

# Copy .env.example to .env
copy .env.example .env

Edit the .env file and add your Azure Document Intelligence credentials:

AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT=https://your-resource-name.cognitiveservices.azure.com/
AZURE_DOCUMENT_INTELLIGENCE_KEY=your-api-key-here

Where to find these values:

• Log in to Azure Portal
• Navigate to your Document Intelligence resource
• Go to "Keys and Endpoint" section
• Copy the endpoint URL and one of the keys

1. Start the web server:

   python app.py

2. Open your browser and navigate to http://localhost:5000
3. Upload a PDF file and select your desired output formats (Text, Markdown, JSON)
4. View the results directly in the browser or download the extracted files

Extract text from a PDF and display it:

python extract_pdf.py path/to/your/document.pdf

python extract_pdf.py input.pdf -o output.txt

Extract with proper markdown formatting including headings, tables, and structure:

python extract_pdf.py document.pdf --markdown -o output.md

This will create a markdown file with:

• Document title and metadata
• Page-by-page content with headings
• Tables formatted as markdown tables
• Key-value pairs formatted as bullet lists

Extract tables, key-value pairs, and paragraphs:

python extract_pdf.py document.pdf --structured

python extract_pdf.py document.pdf --structured -o output.json

positional arguments:
  pdf_file              Path to the PDF file to process

optional arguments:
  -h, --help           Show help message and exit
  -o, --output OUTPUT  Output file path for extracted text
  --markdown           Format output as markdown (with headings, tables, and formatting)
  --structured         Extract structured data (tables, key-value pairs)

📄 Processing PDF: sample.pdf
⏳ Analyzing document...
📑 Processing page 1 of 3
📑 Processing page 2 of 3
📑 Processing page 3 of 3
✅ Extraction complete! Total pages: 3
📊 Total characters extracted: 5432

📄 Extracting structured data from: invoice.pdf
📊 Found 2 table(s)
🔑 Found 15 key-value pair(s)
📝 Found 8 paragraph(s)

==================================================
STRUCTURED DATA SUMMARY
==================================================
Total Pages: 2
Tables: 2
Key-Value Pairs: 15
Paragraphs: 8

If you see an error about missing environment variables, ensure:

1. Your .env file exists in the project root
2. It contains both AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT and AZURE_DOCUMENT_INTELLIGENCE_KEY
3. The values are correct (no extra spaces or quotes)

• Verify your API key is correct
• Ensure your endpoint URL is properly formatted
• Check that your Azure resource is active and not expired

• Ensure the PDF file path is correct
• Use absolute paths if relative paths don't work
• Check file permissions

• Python 3.7+
• Azure Document Intelligence resource (with API key and endpoint)
• Internet connection for API calls

• azure-ai-formrecognizer==3.3.3 - Azure Document Intelligence SDK
• python-dotenv==1.0.0 - Environment variable management

This project is provided as-is for educational and development purposes.