Automated Gate Systems: Container OCR API

The Bottleneck at the Gate

Managing high-throughput automated gate systems (AGS) requires zero bottlenecks. When trucks arrive, traditional optical character recognition often struggles with motion blur, angled captures from gate pillars, and variable outdoor lighting. These failures lead to manual exception handling and severe terminal congestion. To see how our engine cuts through these baseline visual challenges, you can upload a test image to explore our core Container OCR capabilities completely free.

Streamlined API, Zero SDK Bloat

Delivered strictly as a lightweight REST API rather than a heavy SDK, this endpoint integrates directly into your gate logic regardless of your backend language. Powered by our Logistics Container OCR engine, it processes incoming captures in milliseconds. The system natively performs ISO 6346 validation on the fly, instantly calculating check digits to ensure the Container Code is perfectly accurate before triggering the automated barrier.

Gate Automation Scenarios

Integration & AI Prompts

Call the endpoint directly via HTTP. To maintain maximum security and reduce latency at edge nodes, the API flexibly accepts direct multipart file uploads or Base64-encoded strings, avoiding the need to expose gate camera feeds via public URLs.

import requests
import base64

# Prepare Base64 Image from local gate CCTV capture
with open("gate_lane_1_capture.jpg", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

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

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

    if result.get('success'):
        data = result['data']
        print("✅ Container Code Extracted!")
        print(f"Full Number:  {data.get('container_number')}")
        print(f"Valid ISO:    {data.get('is_valid')}")
        print(f"Confidence:   {data.get('confidence')}")
    else:
        print(f"❌ Extraction Failed: {result.get('error')} - {result.get('message')}")

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

{
  "success": true,
  "data": {
    "container_number": "ZCSU8921655",
    "is_valid": true,
    "confidence": "High",
    "parsed": {
      "owner_code": "ZCS",
      "category": "U",
      "serial_number": "892165",
      "check_digit": "5"
    }
  }
}