DocTags Analyzer and Visualizer

AI-powered document analysis and visualization tool for extracting structured content from PDFs.

🚀 Quick Start with Docker

Prerequisites

• Docker and Docker Compose installed
• At least 4GB of free memory
• ~500MB disk space for the AI model

Running with Docker

1. Clone the repository

   git clone <repository-url>
   cd doc-analyzer

2. Place your PDF files in the project directory

   cp /path/to/your/document.pdf ./

3. Start the application

   docker-compose up -d --build

4. Access the web interface

   • Open http://localhost:8080 in your browser
   • Select a PDF from the dropdown
   • Process your documents through the three-step workflow

First Run Notice

⚠️ Important: The first analysis will take 5-10 minutes as the AI model (SmolDocling-256M) needs to be downloaded (~500MB). Subsequent runs will be much faster (30-60 seconds).

📋 Features

• Document Analysis: Extract comprehensive document structure using AI
• Visualization: Generate visual overlays showing document elements
• Image Extraction: Automatically extract and catalog embedded images
• Web Interface: User-friendly interface for document processing

🛠️ Manual Usage

Process PDF pages with DocTags:

python analyzer.py --image document.pdf --page 8 && python visualizer.py --doctags results/output.doctags.txt --pdf document.pdf --page 8 --adjust && python picture_extractor.py --doctags results/output.doctags.txt --pdf document.pdf --page 8 --adjust

🐛 Troubleshooting

Docker Issues

1. Container won't start

   • Check logs: docker-compose logs analyser
   • Ensure ports aren't in use: lsof -i :8080

2. "No module named 'docling_core'" error

   • Rebuild the container: docker-compose down && docker-compose up -d --build

3. Analysis stuck on "Running..."

   • First run downloads the AI model (~500MB), this can take 5-10 minutes
   • Check progress: docker-compose exec analyser du -sh /root/.cache/huggingface/
   • Monitor CPU usage: docker-compose exec analyser ps aux | grep analyzer

4. PDF not loading

   • Ensure poppler is installed (already included in Dockerfile)
   • Place PDFs in the project root directory
   • PDFs must have .pdf extension

Performance Tips

• First analysis is slow due to model download
• Subsequent analyses are much faster (model is cached)
• Processing time depends on PDF complexity and page size
• Monitor memory usage: docker-compose exec analyser free -h

📁 Project Structure

doc-analyzer/
├── backend/
│   ├── page_treatment/     # Core processing scripts
│   │   ├── analyzer.py     # AI-powered document analysis
│   │   ├── visualizer.py   # Visualization generator
│   │   └── picture_extractor.py  # Image extraction
│   ├── app.py             # Flask web application
│   └── requirements.txt   # Python dependencies
├── frontend/              # Web interface
├── results/              # Output directory (auto-created)
├── Dockerfile           # Docker configuration
└── docker-compose.yml   # Docker Compose setup

🔧 Development

To modify the application:

1. Make changes to the code
2. Rebuild the Docker image: docker-compose up -d --build
3. Check logs for errors: docker-compose logs -f analyser

📄 License

This project is open source and available under the MIT License.