Vision-Based Entity Extraction

A computer vision and NLP project that extracts meaningful entities from images using deep learning and OCR techniques.

📋 Overview

This project combines computer vision and natural language processing to identify and extract entities from visual content. It detects objects, text, and structured information within images and converts them into actionable data.

🎯 Objectives

• Extract text and entities from images
• Detect objects and their relationships
• Parse document structure
• Handle multiple entity types
• Process images with varying quality and angles

🗂️ Project Structure

Vision-Based-Entity-Extraction/
├── Vision-Based Entity Extraction.ipynb  # Main notebook
└── README.md                             # Project documentation

🛠️ Technologies & Libraries

• Computer Vision: OpenCV, Pillow, Albumentations
• OCR: EasyOCR, Tesseract, Paddleocr
• Object Detection: YOLO, Faster R-CNN, RetinaNet
• NLP: spaCy, NLTK, Named Entity Recognition (NER)
• Deep Learning: TensorFlow, PyTorch

📊 Key Features

• Text Extraction: OCR from images

• Entity Detection:

  • People names
  • Organizations
  • Locations
  • Dates and times
  • Contact information
  • Product names

• Object Detection: Identify relevant objects in images

• Structured Data Extraction: Parse forms, receipts, documents

• Multi-language Support: Handle various languages

• Confidence Scoring: Reliability metrics for extractions

🚀 Getting Started

Prerequisites

• Python 3.7+
• Jupyter Notebook
• GPU recommended (CUDA 11.0+)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd Vision-Based-Entity-Extraction

2. Install dependencies:

   pip install -r requirements.txt

3. Open the notebook:

   jupyter notebook "Vision-Based Entity Extraction.ipynb"

📈 Pipeline

Input Image
    ↓
Preprocessing (Resize, Normalize)
    ↓
Text Detection & OCR
    ↓
Object Detection
    ↓
Entity Recognition (NER)
    ↓
Relationship Extraction
    ↓
Output (Structured Data)

🔧 Architecture Components

1. Text Detection

• Uses CRAFT or EAST for text localization
• Bounding box extraction
• Perspective correction

2. Text Recognition

• Tesseract or EasyOCR for character recognition
• Language-specific models
• Confidence scoring

3. Entity Recognition

• Pre-trained NER models (spaCy)
• Fine-tuned on domain data
• Custom entity types

4. Post-Processing

• Validation and correction
• Relationship mapping
• Confidence filtering

💾 Training & Configuration

DETECTOR_MODEL = 'CRAFT' or 'EAST'
OCR_BACKEND = 'EasyOCR' or 'Tesseract'
NER_MODEL = 'spacy_model'
CONFIDENCE_THRESHOLD = 0.5
IMAGE_SIZE = (640, 640)

📝 Usage Example

# Initialize components
detector = ImageTextDetector()
recognizer = TextRecognizer()
ner = NamedEntityRecognizer()

# Process image
image = cv2.imread('document.jpg')
text_regions = detector.detect(image)

# Extract text from regions
extracted_data = []
for region in text_regions:
    text = recognizer.recognize(region)
    entities = ner.extract(text)
    extracted_data.append({
        'text': text,
        'entities': entities,
        'confidence': region['confidence']
    })

return extracted_data

📊 Supported Entity Types

• Person: Individual names
• Organization: Company, Institution names
• Location: Places, Addresses
• Date: Temporal expressions
• Money: Currency amounts
• Percent: Percentage values
• Product: Product names
• Email: Email addresses
• Phone: Phone numbers
• URL: Web addresses

⚙️ Performance Metrics

• Text Detection mAP: 85-95%
• Character Recognition Accuracy: 90-98%
• Entity Extraction Accuracy: 80-92%
• Processing Speed: 5-20 FPS (depends on image size)

🔍 Use Cases

• Document Processing: Invoices, receipts, forms
• ID Verification: License plates, ID cards
• Business Card Recognition: Contact extraction
• Document Classification: Automated sorting
• Data Entry Automation: Reduce manual data entry

🎯 Advanced Features

• Multi-language Support: English, Chinese, Arabic, etc.
• Batch Processing: Handle multiple images
• Custom Models: Fine-tune on domain data
• Real-time Processing: Stream video processing
• Quality Assurance: Validation mechanisms

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Submit a pull request

📚 References

• CRAFT: Character Region Awareness for Text Detection
• EAST: An Efficient and Accurate Scene Text Detector
• EasyOCR
• spaCy NER

📄 License

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

🔒 Privacy Considerations

• Handle sensitive personal information securely
• Comply with data protection regulations (GDPR, CCPA)
• Implement proper access controls
• Encrypt sensitive extracted data

✉️ Contact

For questions or suggestions, please open an issue or contact the project maintainers.