Document Parsing Solutions 📄

A comprehensive toolkit for extracting and processing content from PDF documents using various parsing technologies.

Overview

This project implements five different document parsing approaches:

• Unstructured.io API
• Llama Parse
• Mistral OCR
• Azure Document Intelligence
• Amazon Textract

Parser Comparison

1. Unstructured.io API

Strengths:

• Excellent at handling complex document layouts
• Advanced table extraction capabilities
• Maintains document structure and formatting
• Supports multiple document formats

Limitations:

• API rate limits on free tier
• Higher latency due to cloud processing
• Cost increases with document volume

Best For:

• Complex documents with mixed content
• Documents with tables and structured data
• Batch processing requirements

2. Llama Parse

Strengths:

• Strong text extraction capabilities
• Good handling of simple layouts
• Local processing option available
• Efficient for text-heavy documents

Limitations:

• Limited table extraction capabilities
• May struggle with complex layouts
• Requires more computational resources locally

Best For:

• Text-heavy documents
• Simple document layouts
• Local processing requirements

3. Mistral OCR

Strengths:

• Excellent OCR accuracy
• Good language support
• Handles handwritten text well
• Real-time processing capabilities

Limitations:

• Limited formatting preservation
• May struggle with complex tables
• Higher cost for high-volume processing

Best For:

• Documents with handwritten content
• Multi-language documents
• Real-time OCR requirements

4. Azure Document Intelligence

Strengths:

• Advanced AI-powered extraction
• Excellent form field recognition
• Strong table extraction
• Built-in pretraining for common documents

Limitations:

• Azure platform lock-in
• Higher cost for large-scale processing
• Requires Azure subscription

Best For:

• Forms and structured documents
• Enterprise-scale deployments
• Integration with Azure services

5. Amazon Textract

Strengths:

• Excellent table extraction
• Good form field recognition
• Scales well for large volumes
• Strong integration with AWS

Limitations:

• AWS platform lock-in
• Cost can be high for large volumes
• Limited customization options

Best For:

• AWS ecosystem integration
• Large-scale document processing
• Forms and table extraction

🔧 Setup

1. API Keys and Credentials

Unstructured.io

• Sign up at Unstructured.io
• Obtain API key from dashboard

Llama Parse

• Sign up at Llama Cloud
• Generate API key from dashboard

Mistral API

• Visit Mistral AI
• Create account and generate API key

Azure Document Intelligence

• Create resource in Azure Portal
• Get endpoint URL and API key from resource settings

Amazon Textract

• Set up AWS account
• Create IAM user with Textract permissions
• Get AWS access key and secret

2. Environment Setup

1. Install Dependencies

python -m venv venv
source venv/bin/activate  # For Mac/Linux
pip install -r requirements.txt

2. Configure Environment Variables Create a .env file:

# Unstructured.io
UNSTRUCTURED_API_KEY=your_key

# Llama Parse
LLAMA_API_KEY=your_key

# Mistral
MISTRAL_API_KEY=your_key

# Azure
AZURE_ENDPOINT=your_endpoint
AZURE_API_KEY=your_key

# AWS
AWS_ACCESS_KEY_ID=your_key
AWS_SECRET_ACCESS_KEY=your_secret
AWS_REGION=your_region

Usage Guidelines

Document Type Selection

1. Simple Text Documents

   • Recommended: Llama Parse or Unstructured.io
   • Alternative: Mistral OCR

2. Forms and Structured Documents

   • Recommended: Azure Document Intelligence or Amazon Textract
   • Alternative: Unstructured.io

3. Complex Tables

   • Recommended: Amazon Textract or Azure Document Intelligence
   • Alternative: Unstructured.io

4. Handwritten Content

   • Recommended: Mistral OCR
   • Alternative: Azure Document Intelligence

5. Multi-Language Documents

   • Recommended: Mistral OCR or Azure Document Intelligence
   • Alternative: Amazon Textract

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests to our repository.