Monroney Sticker VIN Extraction

The Visual Overload of Monroney Tags

Digitizing new car inventory from the factory window sticker (Monroney label) is an exercise in data overload. These are large-format pages packed with extreme visual hierarchy distractions: massive MSRP pricing, large EPA fuel economy ratings, and dense, multi-column tables detailing standard and optional equipment. The actual 17-character VIN is often printed in a small font, tucked away in a corner or beneath a barcode. Traditional OCR engines attempt to read the entire document top-to-bottom, outputting thousands of useless words and losing the primary identifier in the noise. For a deeper dive into how we handle dense data tables, see our core OCR product capabilities.

StructOCR: Precision Targeting in Dense Data

StructOCR doesn't read the whole page; it hunts for the chassis number. Powered by our flagship Automotive VIN OCR API, it uses spatial intelligence to actively ignore pricing blocks, equipment descriptions, and environmental scores. It scans the document specifically for the alphanumeric structure required by ISO 3779 validation. This ensures that even on an A3-sized sticker packed with text, the API returns the exact 17-character VIN instantly and accurately.

Core Industry Applications

Integration & AI Prompts

Call the endpoint directly via our REST API, or copy the prompt below into Cursor, Lovable, or v0 to instantly generate your frontend application.

import requests
import base64

# 💰 Save 30%+ vs competitors. Get 20 free credits instantly:
# 👉 https://structocr.com/register

# 1. Prepare Base64 Image
with open("monroney_sticker.jpg", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

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

try:
    print("Scanning Monroney Sticker...")
    response = requests.post(url, headers=headers, json=payload)
    result = response.json()

    if result.get('success'):
        data = result['data']
        print("✅ Extraction Successful!")
        print(f"VIN:          {data.get('vin')}")
        print(f"Confidence:   {data.get('confidence')}")
        print(f"Carrier Type: {data.get('carrier_type')}")
    else:
        print(f"❌ Extraction Failed: {result.get('error')} - {result.get('message')}")

except Exception as e:
    print(f"An error occurred: {e}")

{
  "success": true,
  "data": {
    "vin": "WA1UABAG1JX123456",
    "confidence": "High",
    "carrier_type": "document"
  }
}