The Best Python Library for Shipping Container OCR

Stop using regex. Extract structured data from Shipping Containers with 99% accuracy using Python.

Shipping Container OCR extraction process diagram
StructOCR transforms raw Shipping Container images into validated JSON.

The Problem with Shipping Container Parsing

Shipping containers often present challenges for traditional OCR methods. Factors like glare, holograms, skewed angles, varying fonts, and damage degrade the accuracy of regular expression or template-based systems. Vertical text, common in port environments, is especially difficult. These systems struggle to cope with real-world variations, leading to high error rates.

The StructOCR Solution

StructOCR utilizes advanced deep learning models specifically trained on shipping container data, ensuring ISO 6346 compliant extraction is achieved. Our AI automatically handles deskewing, perspective correction, smart cropping, and vertical text reading, providing accurate and reliable results for applications such as logistics and port automation every time.

Common Use Cases

  • KYC/Onboarding: Automate user verification flow.
  • Data Entry Automation: Eliminate manual typing errors.
  • Fraud Detection: Identify inconsistent data fields.

Python SDK Integration

Install the SDK via pip: `pip install structocr`. Then use the following code.

Prerequisite: Python 3.6+ and `structocr` library installed.

CODE EXAMPLE
from structocr import StructOCR
from structocr.exceptions import APIError, InvalidAPIKeyError

# 💰 Save 30%+ vs competitors. Get 20 free credits instantly:
# 👉 https://structocr.com/register
# Initialize with your API Key
client = StructOCR("YOUR_API_KEY_HERE")

def scan_shipping_container():
    # Note: Supports JPG, PNG, WebP (Max 4.5MB)
    # Target: Shipping Container Number (ISO 6346)
    image_path = "container_image.jpg"

    try:
        print(f"Scanning {image_path}...")
        
        # The SDK handles file upload and API communication
        result = client.scan_container(image_path)

        if result and result.get('success'):
            data = result['data']
            print("✅ Container Extraction Successful!")
            
            print(f"Container Number: {data.get('container_number')}")
            print(f"Is Valid (Check Digit): {data.get('is_valid')}")
            print(f"Confidence: {data.get('confidence')}")

            parsed_data = data.get('parsed', {})
            print(f"Owner Code: {parsed_data.get('owner_code')}")
            print(f"Category: {parsed_data.get('category')}")
            print(f"Serial Number: {parsed_data.get('serial_number')}")
            print(f"Check Digit: {parsed_data.get('check_digit')}")

            if data.get('is_valid'):
                print("\nContainer number check digit is valid.")
            else:
                print("\nContainer number check digit is invalid. Please verify.")

        else:
            print(f"❌ Extraction Failed: {result.get('error')}")

    except InvalidAPIKeyError as e:
        print(f"Error: Invalid API Key. {e}")
    except APIError as e:
        print(f"Error communicating with the API: {e}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

if __name__ == "__main__":
    scan_shipping_container()

Technical Specs

  • Latency: < 5s (Average)
  • Uptime: 98.5% SLA
  • Security: AES-256 Encryption & SOC2 Compliant
  • Input: JPG, PNG, WebP (Max 4.5MB)
  • Output: JSON (Structured Data)

Key Features

  • Smart Crop: Auto-detects document boundaries.
  • Field Validation: Cross-validates dates and checksums.
  • Specialized Models: Trained specifically on Shipping Containers.

Sample JSON Response

The API returns a clean JSON object with normalized fields.

{
  "success": true,
  "data": {
    "container_number": "TTNU5194822",
    "is_valid": true,
    "confidence": "High",
    "parsed": {
      "owner_code": "TTN",
      "category": "U",
      "serial_number": "519482",
      "check_digit": "2"
    }
  }
}

Frequently Asked Questions

What file formats are supported?

JPG, PNG, and WebP images up to 4.5MB.

Is data stored?

No. Images are processed in-memory and deleted immediately.

How to handle errors?

Check the 'success' flag and 'error' message in the response.

More OCR Tutorials

Precise Data Extraction and Seamless Integration with AI-powered OCR API.

Empower your solutions with automated data extraction by integrating best-in class StructOCR via API seamlessly.

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