The Premier Python SDK for Receipt Data Extraction

The Machine Learning Bottleneck for Receipts

Data scientists and backend engineers building fintech tools in Python quickly realize that extracting structured data from receipts is a layout nightmare. Relying on local libraries like Pytesseract yields a flat string of text, stripping away crucial spatial relationships. Training a custom LayoutLM or YOLO model to differentiate between a subtotal, a state tax, and a handwritten tip across millions of unique merchant formats demands massive labeled datasets and expensive GPU clusters. Factor in crumpled thermal paper and poor lighting, and local computer vision pipelines simply break down.

The Cloud-Native StructOCR Advantage

The StructOCR Python SDK bypasses this MLOps hurdle entirely. By routing your image payloads to our cloud-native receipt OCR API, our specialized spatial models handle the noise reduction and semantic tagging. You receive a heavily typed Python dictionary instantly. This allows engineering teams to deploy scalable expense management automation features or populate analytics databases without managing local PyTorch or TensorFlow dependencies.

Ideal for Python Financial Workflows

• Fintech ETL Pipelines: Automate the extraction of expense data from bulk S3 bucket uploads, piping the cleansed dictionaries directly into PostgreSQL or Snowflake.
• AI Accounting Assistants: Feed structured line-item data into LLM agents (like LangChain workflows) to automatically categorize spending habits and answer user financial queries.
• Consumer Cashback & Rewards: Systematically scan thousands of user-submitted grocery receipts to identify targeted SKUs and trigger automated reward payouts.

Implementation: Python SDK Usage

Install the SDK via `pip install structocr`. This script demonstrates how to extract expense data and navigate the returned Python dictionary.

Prerequisite: Python 3.7+ and `pip install structocr`

from structocr import StructOCR
import json

# 💰 Save hours of data cleansing. Get 20 free credits instantly:
# 👉 https://structocr.com/register

def process_expense_receipt():
    # Initialize the client with your secret API Key
    client = StructOCR("YOUR_API_KEY_HERE")
    image_path = "./dataset/raw_receipts/coffee_shop_01.jpg"

    try:
        print(f"Analyzing receipt image: {image_path}...")
        
        # The SDK automatically handles file I/O and Base64 encoding
        result = client.scan_receipt(image_path)

        # Verify the API call succeeded and the receipt is valid
        if result.get('success') and result.get('data', {}).get('is_valid'):
            data = result['data']
            print("✅ Receipt Successfully Extracted!")
            print(f"Merchant:   {data.get('merchant_name')} (Confidence: {data.get('confidence')})")
            print(f"Date/Time:  {data.get('date')} {data.get('time')}")
            print(f"Total Paid: {data.get('total_amount')} {data.get('currency')}")
            print(f"Taxes:      {data.get('tax_amount')}\n")
            
            # Iterate through the extracted line items
            print("--- Purchased Items ---")
            for item in data.get('items', []):
                qty = item.get('quantity')
                name = item.get('name')
                price = item.get('price')
                print(f"- {qty}x {name} @ {price}")
            
        else:
            # Handle invalid receipt formats or poor image quality
            error_msg = result.get('data', {}).get('validation_error', 'No recognizable receipt found.')
            print(f"❌ Validation Failed: {error_msg}")

    except Exception as e:
        print(f"SDK or Network Exception: {e}")

if __name__ == "__main__":
    process_expense_receipt()

Sample JSON Dictionary Response

The SDK resolves the request into a native, flat Python dictionary, making key-value extraction incredibly intuitive.

{
  "success": true,
  "data": {
    "is_valid": true,
    "confidence": "high",
    "merchant_name": "Blue Bottle Coffee",
    "date": "2026-04-22",
    "time": "08:45 AM",
    "currency": "USD",
    "total_amount": 14.5,
    "tax_amount": 1.25,
    "items": [
      {
        "name": "Caffe Latte - Large",
        "quantity": 2,
        "price": "11.00"
      },
      {
        "name": "Butter Croissant",
        "quantity": 1,
        "price": "3.50"
      }
    ],
    "validation_error": null
  }
}