Automotive Parts & Aftermarket Workflow Automation

The Challenge: Identifying Exact Vehicle Specifications

In the automotive aftermarket and repair industry, ordering the wrong part is incredibly costly. Vehicles of the same Make and Model often have entirely different engine configurations, brake systems, or trim levels depending on the specific manufacturing month. Mechanics and counter staff frequently struggle to decipher complex registration documents or European standard codes (like P.5 engine codes) to identify the correct compatible components. Learn more about our robust capabilities on our Vehicle Registration OCR product page.

Scan-to-Parts Precision

StructOCR bridges the gap between the physical vehicle and the digital parts catalog. By simply photographing the registration certificate, our engine extracts the precise VIN, displacement, and specific engine codes (often hidden in cryptic fields). These hyper-specific data points, seamlessly captured in our `localized.generic.unmapped_fields`, can be directly fed into parts-matching APIs to guarantee 100% compatibility. Discover how this fits into broader compliance workflows in our Vehicle Registration Solutions guide.

Core Industry Applications

Integration & API Documentation

Easily integrate our full-field OCR API into your business software. 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("automotive-parts-aftermarket.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'))
    
    # Specific workflow 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 Code:", unmapped.get('engine_code'))
        print("✅ Displacement Cc:", unmapped.get('displacement_cc'))

{
  "success": true,
  "data": {
    "document": {
      "country_code": "US",
      "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_code": "CDBA 12345",
          "displacement_cc": "1968",
          "fuel_type": "Diesel"
        }
      }
    }
  }
}