Whole Foods Receipt OCR (United States)

Reading a Whole Foods receipt from United States

Keep weighted produce quantities separate from pack labels. Use the printed row amount for expense totals rather than multiplying a row total a second time. This guide is scoped to United States. Store, checkout and document versions can vary; the merchant name is a search context, not a promise that every receipt has one fixed layout. A successful read of the header is not enough: the amount beside the final payable label and the boundaries of individual item rows are the evidence that makes a useful expense record.

Extract receipt fields without inventing a breakdown

Use the receipt OCR API for merchant_name, date, time, currency, total_amount, tax_amount and items. Each item exposes name, quantity and price; price is the whole-row amount, not necessarily a unit price. The public schema has no dedicated tender, tip, loyalty, QR payload or receipt-number object. Keep the source document when those details matter. Related format guides: United States receipt formatting.

From receipt image to an expense record

Read a receipt from a Python backend

Submit one receipt image using the standard endpoint. Keep the API key server-side and examine quality indicators before mapping the result to an expense. The sample does not claim a live test on this merchant.

import base64
import os
import requests

with open("receipt.jpg", "rb") as source:
    encoded = base64.b64encode(source.read()).decode("ascii")

response = requests.post(
    "https://api.structocr.com/v1/receipt",
    headers={"x-api-key": os.environ["STRUCTOCR_API_KEY"]},
    json={"img": encoded},
    timeout=60,
)
response.raise_for_status()
result = response.json()
if not result.get("success"):
    raise RuntimeError("Extraction was not successful; inspect the response securely")
data = result.get("data") or {}
needs_review = (
    not data.get("is_valid")
    or data.get("status") != "complete"
    or bool(data.get("issues"))
    or data.get("confidence") == "low"
)
# Persist through your own storage layer; avoid logging receipt contents.
print({"needs_review": needs_review, "item_count": len(data.get("items") or [])})

{
  "success": true,
  "data": {
    "response_version": 2,
    "is_valid": true,
    "merchant_name": "Whole Foods",
    "date": "2026-09-01",
    "time": "12:30",
    "currency": "USD",
    "total_amount": 12,
    "tax_amount": 1,
    "items": [
      {
        "name": "APPLES",
        "quantity": 2,
        "price": "8.00"
      },
      {
        "name": "BREAD",
        "quantity": 1,
        "price": "3.00"
      }
    ],
    "confidence": "high",
    "validation_error": null,
    "status": "complete",
    "issues": []
  }
}