Door Jamb & Metal Build Plate VIN OCR

The Challenge of Stamped Door Jamb Plates

Scanning physical compliance or build plates located on a vehicle's door jamb introduces severe optical obstacles. These plates typically feature low-contrast stamped or engraved characters with no ink. Furthermore, because of the tight spaces in door frames, inspectors frequently capture these images sideways or vertically. Combined with physical dirt, metal glare, and competing alphanumeric fields (like Engine or Model codes) explicitly labeled as 'FRAME No.' rather than 'VIN', traditional keyword-based OCR engines simply output garbage text. For a deeper dive into how we handle physical textures, see our core OCR product capabilities.

StructOCR: Built for Physical Textures and Rotations

StructOCR is specifically trained on 'in-the-wild' industrial textures and automatically handles omnidirectional image rotation. It leverages the micro-shadows of stamped letters to reconstruct the text, completely ignoring background metal reflections. Powered by our flagship Automotive VIN OCR engine, it accurately separates the 17-character sequence from surrounding labels like 'MODEL', 'ENGINE', or 'COLOR'. It doesn't rely on the keyword 'VIN'; it applies strict ISO 3779 validation to locate the true chassis sequence and auto-corrects characters obscured by grime.

Core Industry Applications

Integration & AI Prompts

Call the endpoint directly, 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("door_jamb_plate.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 Door Jamb Metal Plate...")
    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": "JTNBX32R1A1234567",
    "confidence": "High",
    "carrier_type": "metal_plate"
  }
}