Intermodal Rail OCR API for North America

The Complexity of Rail Portal Scanning

Capturing container codes on North America's vast intermodal rail networks presents extreme technical hurdles. Rail portal cameras must capture data from double-stacked containers on freight trains moving at high speeds, resulting in motion blur, varying vertical angles, and harsh shadows. Traditional systems fail to maintain high throughput and struggle to isolate text from the corrugated ribs of the container. For a deeper dive into overcoming these environmental hurdles, see our core Container OCR capabilities.

StructOCR: Built for High-Speed Rail Infrastructure

Engineered strictly as a high-performance REST API to ensure seamless integration into railway ERP systems without heavy SDK dependencies, this endpoint is powered by our Logistics Container OCR engine. It natively processes multiple concurrent image streams and handles the varied angles typical of trackside portals. The engine applies strict ISO 6346 validation to verify the owner code and calculate the check digit, guaranteeing absolute data integrity.

Core Applications for North American Railways

Integration & AI Prompts

Call the endpoint directly via HTTP. The API flexibly accepts standard image URLs, direct multipart file uploads, or Base64-encoded strings to minimize network latency on edge networks.

import requests
import base64

# Prepare Base64 Image from high-speed rail portal capture
with open("rail_portal_track4.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 Moving Train Capture...")
    response = requests.post(url, headers=headers, json=payload)
    result = response.json()

    if result.get('success'):
        data = result['data']['parsed']
        print("✅ Rail Container Code Extracted!")
        print(f"Full Number:  {data.get('owner_code')}{data.get('category')}{data.get('serial_number')}-{data.get('check_digit')}")
    else:
        print(f"❌ Extraction Failed: {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"
    }
  }
}