Full-Field South Africa Vehicle Registration (NaTIS Document / RC1) OCR

The Challenge: Dense A4 Layouts and Specific Weight Metrics

The South African vehicle registration certificate (NaTIS Document / RC1) looks more like a dense legal contract than a standard registration card. Printed on A4 paper, it contains a massive amount of monochromatic text, making it difficult for standard bounding-box OCR to isolate the correct fields. Crucially for the African transport and export market, it contains highly specific weight and operational metrics (like Tare, GVM, and GCM) that must be extracted flawlessly for customs and freight compliance. Learn more about our robust capabilities on our Vehicle Registration OCR product page.

Semantic Parsing & Form Logic Understanding

StructOCR abandons traditional layout constraints. Our LLM-backed semantic parsing engine 'reads' the dense NaTIS A4 document, intelligently isolating target data regardless of the print offset. Universal data like the VIN are standardized, while South Africa-specific weight and regulatory metrics are dynamically captured into our `localized.generic.unmapped_fields` object, ensuring no vital export data is lost. Discover how this fits into broader cross-border workflows in our Vehicle Registration Solutions guide.

Core Industry Applications

Integration & API Documentation

Easily integrate our full-field OCR API. For full technical details, visit our Developer API Documentation.

import requests
import base64

# 📸 Test for free with your own image upload: https://structocr.com/developers/vehicle-registration-ocr

with open("south-africa-natis-rc1-full-ocr.jpg", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

url = "https://api.structocr.com/v1/vehicle-registration"
headers = {
    "x-api-key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}
payload = {
    "img": base64_image
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

if result.get('success'):
    data = result.get('data', {})
    std = data.get('standardized', {})
    print("✅ Standardized VIN:", std.get('vin'))
    
    # South Africa specific fields are captured in the generic fallback
    loc = data.get('localized', {})
    if 'generic' in loc and loc['generic'].get('unmapped_fields'):
        unmapped = loc['generic']['unmapped_fields']
        print("✅ Tare:", unmapped.get('tare'))
        print("✅ Gvm:", unmapped.get('gvm'))

{
  "success": true,
  "data": {
    "document": {
      "country_code": "ZA",
      "document_type": "VEHICLE_REGISTRATION",
      "confidence_score": 0.98
    },
    "standardized": {
      "vin": "SAMPLEVIN12345678",
      "plate": {
        "number": "ABC-123",
        "formatted": "ABC 123"
      },
      "make_normalized": "Toyota",
      "year": 2020
    },
    "localized": {
      "generic": {
        "unmapped_fields": {
          "tare": "1150 kg",
          "gvm": "1650 kg",
          "engine_number": "CFN123456"
        }
      }
    }
  }
}