Indonesia Receipt OCR: Formats, Items and Totals

Understand the printed transaction first

Interpret rupiah grouping separators from the complete receipt and distinguish QRIS payment slips from itemized retail receipts. Start by checking whether the document is a completed payment, an estimate, an order confirmation or an account summary. 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: Indomaret receipt OCR, Alfamart receipt OCR.

From receipt image to an expense record

• Check the document-specific layout: Interpret rupiah grouping separators from the complete receipt and distinguish QRIS payment slips from itemized retail receipts.
• Save the purchase once: Associate the image with an application-generated receipt ID. A payment slip and an itemized receipt can describe the same purchase; merchant, date and amount can support a duplicate review but are not a universally unique key.
• Export item rows to CSV or Excel: Create one row per returned item and repeat the parent receipt ID, merchant and date. Sum price only where it represents a resolved row amount; do not multiply it by quantity again.
• Route uncertain records for review: Inspect success, is_valid, status, issues and confidence before accepting the extracted data. Keep unresolved quantities or totals unresolved. An HTTP success code does not establish that every amount is readable.

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.

Prerequisite: Python 3, pip install requests; STRUCTOCR_API_KEY environment variable

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 [])})

Synthetic receipt JSON example

Illustrative values only, not output from a local sample or a merchant benchmark. Item price is a line amount. Currency must be resolved from the submitted receipt rather than hard-coded from this example.

{
  "success": true,
  "data": {
    "response_version": 2,
    "is_valid": true,
    "merchant_name": "Example Merchant",
    "date": "2026-09-01",
    "time": "12:30",
    "currency": "IDR",
    "total_amount": 12,
    "tax_amount": 1,
    "items": [
      {
        "name": "EXAMPLE PURCHASE",
        "quantity": 2,
        "price": "8.00"
      },
      {
        "name": "EXAMPLE CHARGE",
        "quantity": 1,
        "price": "3.00"
      }
    ],
    "confidence": "high",
    "validation_error": null,
    "status": "complete",
    "issues": []
  }
}