Fleet Onboarding Automation Workflow Automation

The Challenge: Commercial Fleet Compliance at Scale

Logistics and commercial transport companies manage thousands of vehicles, each requiring meticulous documentation. Onboarding a new vehicle involves manually typing dense registration data from complex A4 certificates or multi-page foldouts. This is not just slow; it’s a compliance risk. Missing a critical weight class metric (like GVM or Tare) or incorrectly copying a chassis number can lead to severe fines or insurance invalidation across borders. Learn more about our robust capabilities on our Vehicle Registration OCR product page.

Automated Logistics & Compliance Checking

StructOCR transforms fleet onboarding. Instead of manual data entry, operators simply scan or photograph the vehicle registration. Our engine intelligently maps standard fields like VIN and Plate, while simultaneously capturing commercial-specific metrics (such as gross vehicle mass, axles, or special permits) directly into the `localized.generic.unmapped_fields` node. This guarantees 100% accurate digital fleet manifests instantly. 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("fleet-management-onboarding-api.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("✅ Gross Vehicle Mass:", unmapped.get('gross_vehicle_mass'))
        print("✅ Axle Configuration:", unmapped.get('axle_configuration'))

{
  "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": {
          "gross_vehicle_mass": "16000 kg",
          "tare_weight": "6500 kg",
          "axle_configuration": "3 Axle Rigid"
        }
      }
    }
  }
}