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 \ \ / / |_| | | | | |\/| | |    
  \ V /|  _  | | | | |  | | |___ 
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Visual HTML Generator - Convert PDFs to structured HTML with OCR

A modular system for converting PDF documents to structured HTML with advanced OCR and layout analysis capabilities.

• ✨ Features
• 🚀 Quick Start
• 🏗️ Architecture
• 🔧 Installation
• 💻 Usage
• 📚 Documentation
• 🤝 Contributing
• 📄 License

• 🖨️ PDF to image conversion with preprocessing (denoise, deskew, enhance)
• 🔍 Advanced document layout analysis and segmentation
• 🌐 Multi-language OCR support (Polish, English, German, more)
• 🏷️ Automatic document type detection
• 🖥️ Modern, responsive HTML output

• 🔄 Batch processing for multiple documents
• 📊 Metadata extraction and preservation
• 🧩 Modular architecture for easy extension
• 🚀 High-performance processing with parallelization
• 📱 Mobile-responsive output templates
• 🔍 Searchable text output with confidence scoring

• 🐳 Docker support for easy deployment
• 🧪 Comprehensive test suite
• 📦 Well-documented Python API
• 🔌 Plugin system for custom processors

• Python 3.8+
• Tesseract OCR
• Poppler utilities (poppler-utils)
• Git (for development)

# Install system dependencies
sudo apt-get update
sudo apt-get install -y \
    tesseract-ocr \
    tesseract-ocr-pol \
    tesseract-ocr-eng \
    tesseract-ocr-deu \
    poppler-utils \
    python3-pip \
    python3-venv

graph TD
    A[PDF Input] --> B[PDF Processor]
    B --> C[Layout Analyzer]
    C --> D[OCR Engine]
    D --> E[HTML Generator]
    E --> F[Structured HTML Output]
    
    G[Configuration] --> B
    G --> C
    G --> D
    G --> E
    
    H[Plugins] -->|Extend| B
    H -->|Customize| C
    H -->|Enhance| D
    H -->|Theme| E

+----------------+     +-----------------+     +---------------+
|                |     |                 |     |               |
|   PDF Input    |---->|  PDF Processor  |---->| Page Images   |
|                |     |                 |     |               |
+----------------+     +-----------------+     +-------.-------+
                                                    |
                                                    v
+----------------+     +-----------------+     +-------+-------+
|                |     |                 |     |               |
|  HTML Output   |<----|  HTML Generator |<----|  OCR Results  |
|                |     |                 |     |               |
+----------------+     +-----------------+     +-------.-------+
                                                    ^
                                                    |
+----------------+     +-----------------+     +-------+-------+
|                |     |                 |     |               |
| Configuration  |---->| Layout Analyzer |---->| Page Layout   |
|                |     |                 |     |               |
+----------------+     +-----------------+     +---------------+

# 1. Clone the repository
git clone https://github.com/fin-officer/vhtml.git
cd vhtml

# 2. Install Python dependencies
poetry install

# 3. Install system dependencies (if not already installed)
make install-deps

# 4. Verify installation
make validate

# Build the Docker image
docker build -t vhtml .

# Run the container
docker run -v $(pwd)/invoices:/app/invoices -v $(pwd)/output:/app/output vhtml \
    python -m vhtml.main /app/invoices/sample.pdf -o /app/output

To verify that all dependencies are correctly installed:

# Run validation script
make validate

# Or directly
python scripts/validate_installation.py

# Expected output:
# ✓ Python version: 3.8+
# ✓ Tesseract found: v5.0.0
# ✓ Poppler utils installed
# ✓ All Python dependencies satisfied
# ✓ Test document processed successfully

# Process a single PDF file
poetry run python -m vhtml.main /path/to/document.pdf -o output_directory

# Process a directory of PDF files (batch mode)
poetry run python -m vhtml.main /path/to/pdf_directory -b -o output_directory

# Process and open in browser
poetry run python -m vhtml.main /path/to/document.pdf -v

# Specify output format (html/mhtml)
poetry run python -m vhtml.main document.pdf --format mhtml

# Use specific OCR language
poetry run python -m vhtml.main document.pdf --lang pol+eng

from vhtml import DocumentAnalyzer

# Initialize with custom settings
analyzer = DocumentAnalyzer(
    languages=['pol', 'eng'],  # OCR languages
    output_format='html',      # 'html' or 'mhtml'
    debug_mode=False          # Enable debug output
)

# Process a single document
result = analyzer.process("document.pdf", "output_dir")
print(f"Generated: {result.output_path}")
print(f"Metadata: {result.metadata}")

# Batch processing
results = analyzer.process_batch("input_dir", "output_dir")
for result in results:
    print(f"Processed: {result.input_path} -> {result.output_path}")

from vhtml import PDFProcessor, OCREngine

# Load and preprocess PDF
processor = PDFProcessor()
pages = processor.process("document.pdf")

# Perform OCR
ocr = OCREngine(languages=['eng'])
for page_num, page_image in enumerate(pages):
    text = ocr.extract_text(page_image)
    print(f"Page {page_num + 1}:\n{text}\n{'='*50}")

• PDF Processor - Handles PDF to image conversion
• Layout Analyzer - Analyzes document structure
• OCR Engine - Performs text recognition
• HTML Generator - Creates structured HTML output

• Creating Custom Templates
• Adding New Languages
• Performance Tuning
• API Reference

graph LR
    A[Clone Repository] --> B[Install Dependencies]
    B --> C[Run Tests]
    C --> D[Make Changes]
    D --> E[Run Linters]
    E --> F[Update Tests]
    F --> G[Commit Changes]
    G --> H[Create Pull Request]

# Run tests
make test

# Format code
make format

# Run linters
make lint

# Generate documentation
make docs

# Build package
make build

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

• Tesseract OCR for text recognition
• Poppler for PDF processing
• All contributors who helped improve this project

Generate a standalone HTML file with all images, JS, and JSON embedded:

poetry run python examples/pdf2html.py

• Input: Folder with HTML, images, JS, and JSON (e.g., output/mhtml_example/Invoice-30392B3C-0001)
• Output: Standalone HTML (e.g., output/html_example/Invoice-30392B3C-0001_standalone.html)

Generate a fully self-contained MHTML file for browser archiving:

poetry run python examples/pdf2mhtml.py

• Input: PDF(s) in invoices/ (or other test files)
• Output: MHTML file (e.g., output/mhtml_example/Invoice-30392B3C-0001.mhtml)

• See examples/html.py and examples/mhtml.py for usage patterns and batch processing.
• Both scripts demonstrate how to use the vHTML API for document conversion and archiving.

• PDFProcessor: Handles PDF to image conversion and preprocessing
• LayoutAnalyzer: Analyzes document layout and segments content blocks
• OCREngine: Performs OCR with language detection and confidence scoring
• HTMLGenerator: Generates HTML with embedded images and styling
• DocumentAnalyzer: Integrates all components into a complete workflow

vhtml/
├── vhtml/
│   ├── core/
│   │   ├── pdf_processor.py
│   │   ├── layout_analyzer.py
│   │   ├── ocr_engine.py
│   │   └── html_generator.py
│   └── main.py
├── scripts/
│   ├── validate_installation.py
│   └── test_integration.py
├── docs/
│   ├── ARCHITECTURE.md
│   ├── IMPLEMENTATION.md
│   └── PROJECT_STRUCTURE.md
├── Makefile
├── pyproject.toml
└── README.md

# Setup development environment
make setup

# Run tests
make test

# Format code
make format

# Lint code
make lint

# Build package
make build

For more detailed information, see the documentation files:

• Architecture
• Implementation
• Project Structure

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.