Full-Field India Vehicle Registration (RC Book / Smart Card) OCR

The Challenge: Extreme State Fragmentation and Glare

India's vehicle registration landscape is notoriously fragmented. Documents range from legacy paper 'RC Books' to modern, highly reflective plastic Smart Cards. Layouts, fonts, and even secondary languages differ across all 28 states and 8 union territories. Plastic cards produce extreme glare in smartphone photos, while old RC books are often torn and faded. Traditional template-based OCR systems cannot scale in this chaotic environment. Learn more about our capabilities on our Vehicle Registration OCR product page.

Template-Free Extraction & Glare Reduction

StructOCR abandons brittle templates. Our LLM-backed semantic parsing engine dynamically understands RC data regardless of the state's layout. Furthermore, our visual grounding models actively filter out smart card glare and hologram reflections. Critical VAHAN-compatible data points are mapped universally, while unique state identifiers fall safely into our `localized.generic.unmapped_fields` object. Discover how this fits into your 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("india-rc-book-smart-card-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'))
    
    # India 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("✅ Engine Number:", unmapped.get('engine_number'))
        print("✅ Registration Authority:", unmapped.get('registration_authority'))

{
  "success": true,
  "data": {
    "document": {
      "country_code": "IN",
      "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": {
          "engine_number": "G4LA123456",
          "registration_authority": "MH-01 Mumbai",
          "vehicle_class": "LMV"
        }
      }
    }
  }
}