vhtml

PDF to HTML Conversion System with OCR

1. System Architecture

Core Components:

• PDF Processor - converts PDF documents to images
• Layout Analyzer - analyzes page layout and segments content blocks
• OCR Engine - performs text recognition in content blocks
• Language Detector - identifies document language and format
• HTML Generator - creates structured HTML with metadata
• Template Engine - provides templates for different document types

2. Technology Stack

Core Libraries:

• OpenCV - image processing and segmentation
• Tesseract/EasyOCR - optical character recognition
• pdf2image - PDF to image conversion
• langdetect - language detection
• Jinja2 - HTML templating
• scikit-image - advanced image operations

Additional Tools:

• PyMuPDF (fitz) - alternative PDF processing
• layoutparser - pre-trained layout analysis models
• spaCy - text analysis and NER
• Pillow - image manipulation

3. System Workflow

Step 1: PDF Preprocessing

PDF → Page Images → Preprocessing (denoising, deskewing) → Layout Analysis

Step 2: Block Segmentation

Image → Text Block Detection → Block Classification → Block Hierarchy

Step 3: OCR and Analysis

Block → OCR → Language Detection → Format Analysis → Metadata

Step 4: HTML Generation

Block Structure + Text + Metadata → HTML Template → Final HTML

4. Document Types and Templates

Invoice Template (4 blocks):

• Block A (top left) - sender information
• Block B (top right) - recipient information
• Block C (middle) - line items table
• Block D (bottom) - payment summary

6-Column Template:

• Blocks A,B (top row)
• Blocks C,D (middle row)
• Blocks E,F (bottom row)

Universal Template:

• Automatic block count detection
• Adaptive segmentation

5. Metadata Structure

{
  "document": {
    "type": "invoice|form|letter|other",
    "language": "pl|en|de",
    "layout": "4-block|6-block|custom",
    "confidence": 0.95
  },
  "blocks": [
    {
      "id": "A",
      "type": "header|content|table|footer",
      "position": {"x": 0, "y": 0, "width": 300, "height": 200},
      "content": "recognized text",
      "language": "en",
      "confidence": 0.92,
      "formatting": {
        "bold": [0, 10],
        "tables": [],
        "lists": []
      }
    }
  ]
}