To process receipts with an OCR API and turn them into structured fields, you generally want a pipeline like this:

1) Capture the receipt image

Get the receipt as one of:

• JPG / PNG photo
• PDF scan
• multi-page PDF if needed

Best results come from:

• good lighting
• flat, unwrinkled paper
• full receipt in frame
• high resolution

2) Send it to an OCR API

Use an OCR service that supports either:

• plain text extraction, or
• document/receipt parsing with built-in fields

Examples of APIs that can do this:

• Google Cloud Vision / Document AI
• AWS Textract
• Azure Document Intelligence
• Mindee
• Veryfi
• OCR.space
• ABBYY
• Nanonets

If the API has a receipt endpoint, use that instead of generic OCR, because it can often return structured fields directly.

3) Extract structured fields

Typical receipt fields you may want:

• merchant / store name
• date and time
• subtotal
• tax
• tip
• total
• currency
• payment method
• line items:
  • item name
  • quantity
  • unit price
  • total price
• receipt number / transaction ID

Example JSON structure

{
  "merchant_name": "ABC Grocery",
  "date": "2026-10-11",
  "time": "14:32",
  "currency": "USD",
  "subtotal": 18.49,
  "tax": 1.48,
  "total": 19.97,
  "line_items": [
    {
      "description": "Milk",
      "quantity": 1,
      "unit_price": 3.99,
      "amount": 3.99
    },
    {
      "description": "Bread",
      "quantity": 2,
      "unit_price": 2.25,
      "amount": 4.50
    }
  ]
}

4) Post-process and validate

OCR output is often noisy, so you should:

• normalize dates
• convert currency strings to decimals
• validate totals: subtotal + tax (+ tip) ≈ total
• handle missing fields
• deduplicate repeated text
• fix common OCR mistakes like O vs 0, S vs 5

5) Use confidence scores

Many OCR APIs return confidence values per field or word. You can:

• trust high-confidence fields directly
• flag low-confidence fields for review
• fall back to rules or manual correction

---

Two common approaches

A) Use a receipt-specific OCR API

Best if you want structured fields quickly.

Example flow:

1. Upload receipt image
2. API returns JSON with parsed fields
3. Store JSON in your database

Pros:

• less custom code
• better for receipts/invoices
• structured output

Cons:

• vendor lock-in
• cost per document

---

B) Use generic OCR + your own parser

Best if you need flexibility or already have OCR text.

Example flow:

1. OCR API returns raw text
2. Parse text with regex / heuristics / NLP
3. Map to fields

Pros:

• flexible
• cheaper in some cases

Cons:

• more engineering effort
• less accurate for complex layouts

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Example pseudo-code

Python example

import requests

url = "https://api.example.com/receipt-ocr"
headers = {
    "Authorization": "Bearer YOUR_API_KEY"
}

with open("receipt.jpg", "rb") as f:
    files = {"file": f}
    response = requests.post(url, headers=headers, files=files)

data = response.json()

print(data["merchant_name"])
print(data["total"])
print(data["line_items"])

---

If you only get raw OCR text

You can parse it with rules like:

import re

text = """
ABC Grocery
2026-10-11 14:32
Subtotal 18.49
Tax 1.48
Total 19.97
"""

total_match = re.search(r"Total\s+(\d+\.\d{2})", text, re.I)
if total_match:
    total = float(total_match.group(1))
    print(total)

For line items, you may need more advanced parsing because receipts vary a lot in format.

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Best practices

• Prefer receipt/document parsing APIs over plain OCR if you need structured fields.
• Use image preprocessing:
  • deskew
  • crop
  • increase contrast
  • denoise
• Keep a human review step for low-confidence receipts.
• Normalize everything into a standard schema.
• Store both raw OCR output and cleaned structured data.

---

Recommended architecture

1. Upload receipt
2. Preprocess image
3. Call OCR/receipt API
4. Parse structured response
5. Validate fields
6. Store in DB
7. Send low-confidence items to review

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If you want, I can also give you:

• a Python example using a real OCR API
• a Node.js example
• or a recommended receipt JSON schema for your app.