111 lines
3.6 KiB
Markdown
111 lines
3.6 KiB
Markdown
# DocTags Analyzer and Visualizer
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AI-powered document analysis and visualization tool for extracting structured content from PDFs.
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<img width="800" height="685" alt="image" src="https://github.com/user-attachments/assets/ce301175-ea4b-42e8-8e3a-ff9c326b84cd" />
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## 🚀 Quick Start with Docker
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### Prerequisites
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- Docker and Docker Compose installed
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- At least 4GB of free memory
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- ~500MB disk space for the AI model
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### Running with Docker
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1. **Clone the repository**
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```bash
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git clone <repository-url>
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cd doc-analyzer
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```
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2. **Place your PDF files in the project directory**
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```bash
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cp /path/to/your/document.pdf ./
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```
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3. **Start the application**
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```bash
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docker-compose up -d --build
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```
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4. **Access the web interface**
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- Open http://localhost:8080 in your browser
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- Select a PDF from the dropdown
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- Process your documents through the three-step workflow
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### First Run Notice
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⚠️ **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).
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## 📋 Features
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- **Document Analysis**: Extract comprehensive document structure using AI
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- **Visualization**: Generate visual overlays showing document elements
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- **Image Extraction**: Automatically extract and catalog embedded images
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- **Web Interface**: User-friendly interface for document processing
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## 🛠️ Manual Usage
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Process PDF pages with DocTags:
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```bash
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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
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```
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## 🐛 Troubleshooting
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### Docker Issues
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1. **Container won't start**
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- Check logs: `docker-compose logs analyser`
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- Ensure ports aren't in use: `lsof -i :8080`
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2. **"No module named 'docling_core'" error**
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- Rebuild the container: `docker-compose down && docker-compose up -d --build`
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3. **Analysis stuck on "Running..."**
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- First run downloads the AI model (~500MB), this can take 5-10 minutes
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- Check progress: `docker-compose exec analyser du -sh /root/.cache/huggingface/`
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- Monitor CPU usage: `docker-compose exec analyser ps aux | grep analyzer`
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4. **PDF not loading**
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- Ensure poppler is installed (already included in Dockerfile)
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- Place PDFs in the project root directory
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- PDFs must have `.pdf` extension
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### Performance Tips
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- First analysis is slow due to model download
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- Subsequent analyses are much faster (model is cached)
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- Processing time depends on PDF complexity and page size
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- Monitor memory usage: `docker-compose exec analyser free -h`
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## 📁 Project Structure
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```
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doc-analyzer/
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├── backend/
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│ ├── page_treatment/ # Core processing scripts
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│ │ ├── analyzer.py # AI-powered document analysis
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│ │ ├── visualizer.py # Visualization generator
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│ │ └── picture_extractor.py # Image extraction
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│ ├── app.py # Flask web application
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│ └── requirements.txt # Python dependencies
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├── frontend/ # Web interface
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├── results/ # Output directory (auto-created)
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├── Dockerfile # Docker configuration
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└── docker-compose.yml # Docker Compose setup
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```
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## 🔧 Development
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To modify the application:
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1. Make changes to the code
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2. Rebuild the Docker image: `docker-compose up -d --build`
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3. Check logs for errors: `docker-compose logs -f analyser`
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## 📄 License
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This project is open source and available under the MIT License.
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