Smart OCR Document Scanner

What This Does

This workflow enables intelligent text extraction from images and scanned documents using PaddleOCR - a leading OCR engine supporting 100+ languages. Extract text from photos, screenshots, scanned PDFs, and handwritten documents with high accuracy.

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Quick Start

Step 1: Create a Project Folder

mkdir -p ~/Documents/SmartOcr

Step 2: Download the Template

Click Download above, then:

mv ~/Downloads/CLAUDE.md ~/Documents/SmartOcr/

Step 3: Start Working

cd ~/Documents/SmartOcr
claude

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How to Use

1. Provide the image or scanned document
2. Optionally specify language(s) to detect
3. I'll extract text with position and confidence data

Example prompts:

• "Extract all text from this screenshot"
• "OCR this scanned PDF document"
• "Read the text from this business card photo"
• "Extract Chinese and English text from this image"

Best Practices

1. Preprocess Images: Improve quality before OCR
2. Choose Correct Language: Specify language for better accuracy
3. Handle Multi-column: Process columns separately
4. Filter Low Confidence: Skip results below threshold
5. Batch Processing: Process multiple images efficiently

Examples

Example 1: Business Card Reader

from paddleocr import PaddleOCR
import re

def read_business_card(image_path):
    """Extract contact info from business card."""
    
    ocr = PaddleOCR(use_angle_cls=True, lang='en')
    result = ocr.ocr(image_path)
    
    # Extract all text
    all_text = []
    for line in result[0]:
        all_text.append(line[1][0])
    
    full_text = '\n'.join(all_text)
    
    # Parse contact info
    contact = {
        'name': None,
        'email': None,
        'phone': None,
        'company': None,
        'title': None,
        'raw_text': full_text
    }
    
    # Email pattern
    email_match = re.search(r'[\w\.-]+@[\w\.-]+\.\w+', full_text)
    if email_match:
        contact['email'] = email_match.group()
    
    # Phone pattern
    phone_match = re.search(r'[\+\d][\d\s\-\(\)]{8,}', full_text)
    if phone_match:
        contact['phone'] = phone_match.group().strip()
    
    # Name is usually the largest/first text
    if all_text:
        contact['name'] = all_text[0]
    
    return contact

card_info = read_business_card('business_card.jpg')
print(f"Name: {card_info['name']}")
print(f"Email: {card_info['email']}")
print(f"Phone: {card_info['phone']}")

Example 2: Receipt Scanner

from paddleocr import PaddleOCR
import re

def scan_receipt(image_path):
    """Extract items and total from receipt."""
    
    ocr = PaddleOCR(use_angle_cls=True, lang='en')
    result = ocr.ocr(image_path)
    
    lines = []
    for line in result[0]:
        text = line[1][0]
        y_pos = line[0][0][1]
        lines.append({'text': text, 'y': y_pos})
    
    # Sort by vertical position
    lines.sort(key=lambda x: x['y'])
    
    receipt = {
        'items': [],
        'subtotal': None,
        'tax': None,
        'total': None
    }
    
    for line in lines:
        text = line['text']
        
        # Look for total
        if 'total' in text.lower():
            amount = re.search(r'\$?([\d,]+\.?\d*)', text)
            if amount:
                if 'sub' in text.lower():
                    receipt['subtotal'] = float(amount.group(1).replace(',', ''))
                else:
                    receipt['total'] = float(amount.group(1).replace(',', ''))
        
        # Look for tax
        elif 'tax' in text.lower():
            amount = re.search(r'\$?([\d,]+\.?\d*)', text)
            if amount:
                receipt['tax'] = float(amount.group(1).replace(',', ''))
        
        # Look for items (line with price)
        else:
            item_match = re.search(r'(.+?)\s+\$?([\d,]+\.?\d+)$', text)
            if item_match:
                receipt['items'].append({
                    'name': item_match.group(1).strip(),
                    'price': float(item_match.group(2).replace(',', ''))
                })
    
    return receipt

receipt_data = scan_receipt('receipt.jpg')
print(f"Items: {len(receipt_data['items'])}")
print(f"Total: ${receipt_data['total']}")

Example 3: Multi-language Document

Limitations

• Handwritten text accuracy varies
• Very small text may not be detected
• Complex backgrounds reduce accuracy
• Rotated text needs angle classification
• GPU recommended for best performance

Installation

# CPU version
pip install paddlepaddle paddleocr

# GPU version (CUDA 11.x)
pip install paddlepaddle-gpu paddleocr

# Additional dependencies
pip install pdf2image Pillow