Tanzania NIDA Card OCR Python SDK

Instantly extract data and MRZ from Tanzanian IDs using our native Python library.

AI extracting data from a Tanzania ID card
StructOCR engine analyzing a Tanzanian document and MRZ lines in real-time.

Parsing NIDA Card Challenges

Tanzanian NIDA cards present unique OCR challenges. First, layout variations across different card versions require robust template matching. Second, varying print quality and potential image degradation can hinder accurate character recognition.

Why StructOCR for Tanzania

Our OCR model is specifically trained on a large dataset of Tanzanian NIDA cards to accurately extract data despite layout variations and imperfect image quality. The StructOCR Python SDK simplifies integration, allowing developers to seamlessly incorporate national id ocr into their applications. Furthermore, it fully supports reading the Machine Readable Zone (MRZ) for strict global compliance and identity verification. Our id parsing api is designed for efficient information parsing of identity documents.

Common Use Cases in Tanzania

  • Digital Onboarding: Verify users for Fintech apps in Tanzania.
  • Telecom Registration: Automate SIM card registration with NIDA Card.
  • Hotel Check-in: Speed up guest registration workflows.

Live Demo: ID card scanner

No registration required. Upload a file to test the extraction.

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Python SDK Integration

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

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

from structocr import StructOCR

# 🔑 Need a key for Tanzania? Get 200 free credits instantly:
# 👉 https://structocr.com/register
# Initialize with your API Key
client = StructOCR("YOUR_API_KEY_HERE")

def scan_tanzania_id():
    # Note: Supports JPG, PNG, WebP (Max 4.5MB)
    # Target: NIDA Card
    image_path = "tanzania_national_id.jpg"

    try:
        print(f"Scanning {image_path}...")
        
        # The SDK handles file upload and API communication
        # It automatically detects that this is a Tanzanian document
        result = client.scan_national_id(image_path)

        # Check success flag (SDK returns a dict matching the JSON response)
        if result.get('success'):
            data = result['data']
            print("✅ Tanzania Extraction Successful!")
            
            # Basic Identity
            print(f"Region:      {data.get('country_code')} (Series: {data.get('card_series')})")
            print(f"Name:        {data.get('given_names')} {data.get('surname')}")
            print(f"ID Number:   {data.get('document_number')}")
            
            # Critical Field: Personal Identity Number (CNP/CPF/NIN)
            print(f"Personal #:  {data.get('personal_number')}")
            
            # Demographics
            print(f"DOB:         {data.get('date_of_birth')} ({data.get('sex')})")
            print(f"Address:     {data.get('address')}")

            # Machine Readable Zone (MRZ)
            additional = data.get('additional_fields', {})
            if additional.get('mrz_line_1'):
                print("\n--- MRZ Data ---")
                print(additional.get('mrz_line_1'))
                print(additional.get('mrz_line_2'))
                if additional.get('mrz_line_3'):
                    print(additional.get('mrz_line_3'))
        else:
            print(f"❌ Extraction Failed: {result.get('error')}")

    except Exception as e:
        # Handle SDK or Network errors
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    scan_tanzania_id()

Technical Specs

  • Latency: < 4s (Average)
  • Uptime: 99.9% SLA
  • Security: AES-256 Encryption & SOC2 Compliant
  • Input: JPG, PNG, WebP (Max 4.5MB)
  • Output: JSON (Structured Data)

Key Features

  • Native Script Support: Reads English and local characters.
  • MRZ Parsing: Accurately extracts and validates the Machine Readable Zone lines for cross-checking.
  • Blur Detection: Automatically rejects blurry images.
  • Fraud Check: Validates NIDA Card number format.
  • Smart Crop: Removes background noise automatically.

JSON Response Example

The SDK returns a Python dictionary matching this JSON structure.

{
  "success": true,
  "data": {
    "type": "national_id",
    "country_code": "TZA",
    "nationality": "TANZANIAN",
    "document_number": "19900101-12345-00001-2",
    "card_series": "",
    "personal_number": "19900101-12345-00001-2",
    "surname": "KIMARO",
    "given_names": "JOHN",
    "sex": "M",
    "date_of_birth": "1990-05-15",
    "place_of_birth": "DAR ES SALAAM",
    "address": "KINONDONI, DAR ES SALAAM",
    "date_of_issue": "2020-01-01",
    "date_of_expiry": "2030-01-01",
    "issuing_authority": "NIDA",
    "additional_fields": {
      "phone_number": null,
      "tramite_number": null,
      "ejemplar": null,
      "mrz_line_1": "IDTZA19900101<12345000012<<<<<",
      "mrz_line_2": "9005156M3001014TZA<<<<<<<<<<<2",
      "mrz_line_3": "KIMARO<<JOHN<<<<<<<<<<<<<<<<<<"
    }
  }
}

Frequently Asked Questions

Does the Python SDK handle image uploads?

Yes, the SDK automatically handles base64 encoding and file uploads.

Is data stored?

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

How to handle errors?

The SDK result dictionary contains a 'success' boolean and an 'error' message if failed.

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