Railway Portals: Container OCR API

The Speed and Motion Blur Bottleneck

Capturing container codes on moving freight trains presents severe optical challenges. As intermodal trains pass through trackside portals at speed, cameras suffer from significant motion blur and rolling shutter effects. Additionally, double-stacked well-cars create complex visual backgrounds. Standard OCR fails to keep up with the continuous frame rate, causing missing data in your railway manifest. To see how our engine cuts through these extreme dynamic challenges, you can upload a test image to explore our core Container OCR capabilities completely free.

High-Speed Extraction via Edge API

Delivered strictly as a high-throughput REST API to ensure seamless integration into any railway portal system without heavy SDK dependencies, this endpoint is powered by our Logistics Container OCR engine. It utilizes advanced deep-learning filters specifically tuned to reconstruct characters distorted by motion blur. The engine applies strict ISO 6346 validation on the fly, instantly calculating check digits to guarantee perfect data integrity before passing the railcar manifest to your intermodal operating system.

Railway & Intermodal Scenarios

Integration & AI Prompts

Call the endpoint directly via HTTP. The API flexibly accepts standard image URLs, direct multipart file uploads, or Base64-encoded strings, making it perfect for edge-computing gateways installed trackside.

import requests
import base64

# Prepare Base64 Image from trackside portal camera
with open("train_portal_cam_3.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 Railway 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"
    }
  }
}