The Python SDK for 99.8%+ Accurate Invoice Data Extraction

Why Production-Ready Invoice OCR is Difficult

Open-source tools like Tesseract fail in production because invoices lack a standard format. Each vendor uses a unique layout, making template-based or regex-based parsing brittle and costly to maintain. Core challenges include accurately identifying and extracting tabular data (line items) which can span multiple pages, parsing various date formats (MM/DD/YY vs DD-MM-YYYY), and handling low-quality inputs from scanners which introduce skew, rotation, and digital noise. This forces engineering teams into a perpetual cycle of building and fixing custom parsers for each new vendor, a fundamentally unscalable approach.

Enterprise-Grade Extraction with StructOCR

StructOCR leverages pre-trained Deep Learning models engineered for invoice and receipt documents, offering a powerful accounts payable ocr solution. Our API bypasses template dependency by understanding document semantics. All inputs undergo automatic image pre-processing for deskewing and denoising. The models then precisely identify key-value pairs and table boundaries for accurate line item extraction. Unlike traditional OCR engines that output unstructured text, StructOCR provides a standardized JSON, delivering clean data structures optimized for direct integration into your AP systems and enhancing finance workflows.

Production Use Cases

• Accounts Payable Automation: Automate your entire AP workflow from document ingestion to ERP entry. Reduce invoice processing costs by over 80% and eliminate human error.
• Expense Management Automation: Instantly capture receipt and invoice data for real-time expense reporting and approval, accelerating reimbursement cycles.
• Supply Chain Finance: Extract purchase order numbers, payment terms, and line items to verify trade documents and accelerate supply chain finance operations.

Implementation: Python SDK

The official Python SDK simplifies the integration by handling file encoding and authentication. It parses the nested JSON response (merchant, financials, line items) automatically.

Prerequisite: pip install structocr

from structocr import StructOCR

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# 👉 https://structocr.com/register
# Initialize with your API Key
client = StructOCR("YOUR_API_KEY_HERE")

def process_invoice():
    # Note: Currently supports image inputs (JPG, PNG)
    image_path = "invoice.jpg"

    try:
        print(f"Scanning invoice: {image_path}...")
        
        # The SDK handles the API request and error mapping
        result = client.scan_invoice(image_path)

        # Access the structured data
        data = result['data']
        
        print("✅ Extraction Successful!")
        print(f"Invoice #: {data.get('invoice_number')}")
        print(f"Date: {data.get('date')} (Due: {data.get('due_date')})")
        
        # Merchant Details
        merchant = data.get('merchant', {})
        print(f"Vendor: {merchant.get('name')} (Tax ID: {merchant.get('tax_id')})")
        
        # Financials
        fin = data.get('financials', {})
        print(f"Total: {fin.get('total_amount')} {data.get('currency')}")
        print(f"Tax: {fin.get('tax_amount')}")

        # Line Items Table
        print("\n--- Line Items ---")
        for item in data.get('line_items', []):
            print(f"- {item.get('description')}: {item.get('quantity')} x {item.get('unit_price')} = {item.get('amount')}")

    except Exception as e:
        print(f"❌ Extraction Failed: {e}")

if __name__ == "__main__":
    process_invoice()

Sample JSON Output

StructOCR returns a normalized JSON object, regardless of the input invoice layout, angle, or quality.

{
  "success": true,
  "data": {
    "type": "invoice",
    "invoice_number": "INV-2026-001",
    "date": "2026-01-15",
    "due_date": "2026-02-15",
    "currency": "USD",
    "merchant": {
      "name": "AWS Web Services",
      "address": "410 Terry Ave N, Seattle, WA",
      "tax_id": "EIN-12-3456789",
      "iban": null
    },
    "customer": {
      "name": "Acme Corp Inc.",
      "tax_id": "987654321"
    },
    "financials": {
      "subtotal": 100,
      "tax_amount": 10,
      "total_amount": 110
    },
    "line_items": [
      {
        "description": "EC2 Instance Usage",
        "quantity": 1,
        "unit_price": 80,
        "amount": 80
      },
      {
        "description": "S3 Storage",
        "quantity": 1,
        "unit_price": 20,
        "amount": 20
      }
    ]
  }
}