Tabscanner Receipt OCR API Vs top tier competition
June 18, 2025 Michael Lloyd

Last Updated on June 19, 2025

This is not the kind of comparison we see on so many receipt OCR API websites. It was done by the closest thing to a neutral 3rd party: ChatGPT. We invite you to try it for yourselves by asking it to compare the top receipt OCR APIs at ChatGPT.com

What is the best Receipt OCR API?

TL:DR: Tabscanner equals or beats the competition in every category. It is only when you start looking outside of the parameters that you would consider alternatives. For example, some companies also have a mobile app, many process different types of documents. We specialise in receipts (and similar invoices). Others tend to support more documents.

 

Feature / Engine Tabscanner Mindee Google Vision OCR AWS Textract Veryfi ABBYY FlexiCapture
POS Receipt Accuracy ⭐️⭐️⭐️⭐️⭐️ (99%+) ⭐️⭐️⭐️⭐️ (95–97%) ⭐️⭐️⭐️ (85–90%) ⭐️⭐️⭐️ (85–90%) ⭐️⭐️⭐️⭐️ (92–95%) ⭐️⭐️⭐️⭐️ (92–95%)
Line-Item Extraction ✅ Full + taxes, totals ✅ Partial / custom ❌ (raw text only) ❌ (requires post-process) ✅ Real-time parsing ✅ With rule config
Multi-Language ✅ Global (100+ langs) ✅ Major languages ✅ 100+ languages ✅ Major languages ✅ (limited languages) ✅ (high config needed)
Receipt Format Support ✅ All (global POS) ⚠️ Mostly structured ❌ Struggles with mixed ❌ Template-based limits ⚠️ Mostly US receipts ⚠️ Template-driven
Cloud API ✅ Yes (JSON) ✅ Yes (REST) ✅ Yes ✅ Yes ✅ Yes ✅ (via SDK/API)
Real-Time Processing ✅ Fast (<1s typical) ✅ Fast (~1–2s) ✅ Near instant ✅ ~2s ✅ Slower ❌ Slower, batch-first
Customization Options ✅ AI tuned per client ⚠️ Minimal tuning ❌ None ⚠️ Basic configs ⚠️ Fixed pipeline ✅ Highly configurable
Best For POS receipts globally SaaS/Dev integrations General OCR tasks Forms/doc OCR Expense apps Enterprise capture
Pricing (est.) 💰 Competitive (usage)* 💰 Mid-range 💸 Cheap at scale 💸 Cheap at scale 💰 Subscription tiers 💸 Enterprise licenses

*Tabscanner also offers a long term free plan for developers and start-ups. At scale it is unlikely the others will be as low cost. Tabscanner also gets extra data like the merchant name more often than the others.

The above data is taken from ChatGPT. There has been no independent testing. The accuracy figures look high, especially when the most visible option on Google has a 90% accuracy rate. We ask why they were left out by ChatGPT at the end of this piece.

 

Best For…

Winner

Why
Highest Accuracy ⭐ Tabscanner Purpose-built and trained only on receipts
Mobile Real-time Use ⭐ Veryfi Fast SDKs, good for smartphone scanning
Low-Cost General OCR ⭐ Google Vision Cheap, decent for text blocks
Scalable AWS Workflows ⭐ Textract Deep integration with AWS services
Privacy + Compliance ⭐ Tabscanner / Veryfi Strong GDPR / PCI / HIPAA alignment
Handwritten Receipt Totals ⭐ Tabscanner Few tools support this accurately
Enterprise Receipt Automation ⭐ Tabscanner Used by FinTech and bookkeeping platforms

 

✅ Verdict:

If your core need is accurate, structured receipt data extraction (especially line items, tax, and totals) — Tabscanner is the most specialized and accurate option available.

  • For mobile-first apps, Veryfi is also a solid choice.

  • For general OCR of mixed document types, Google Vision OCR or Textract work, but require manual effort to process receipts effectively.

 

Example receipt OCR Output comparisons

Here’s an example of how Tabscanner, Veryfi, Google Vision OCR, and AWS Textract would typically parse the same simple supermarket receipt.

Sample Receipt

Walmart Superstore  
123 Main Street  
Date: 2024-11-10 14:35  
--------------------------------  
Milk 2L         $3.50  
Bread           $2.00  
Eggs 12-pack    $4.25  
--------------------------------  
Subtotal        $9.75  
Tax             $0.78  
Total           $10.53  
Payment: VISA

Tabscanner

{
  "merchant_name": "Walmart Superstore",
  "address": "123 Main Street",
  "date": "2024-11-10T14:35:00",
  "items": [
    { "description": "Milk 2L", "price": 3.50 },
    { "description": "Bread", "price": 2.00 },
    { "description": "Eggs 12-pack", "price": 4.25 }
  ],
  "subtotal": 9.75,
  "tax": 0.78,
  "total": 10.53,
  "payment_method": "VISA"
}

  • ✅ Highly structured

  • ✅ Accurate merchant, date, and line-items

  • ✅ Parses totals and taxes correctly

  • ✅ Recognizes payment method

Google Vision OCR

{
  "text": "Walmart Superstore\n123 Main Street\nDate: 2024-11-10 14:35\n...\nMilk 2L $3.50\nBread $2.00\nEggs 12-pack $4.25\n...\nTotal $10.53\nPayment: VISA",
  "blocks": [ ... ]
}

  • ❌ Raw text only, no structured fields

  • ❌ No parsing of line-items or totals

  • ⚠️ Requires custom post-processing logic to extract data 

 

Veryfi

{
  "vendor": {
    "name": "Walmart Superstore",
    "address": "123 Main Street"
  },
  "date": "2024-11-10",
  "line_items": [
    { "description": "Milk 2L", "amount": 3.50 },
    { "description": "Bread", "amount": 2.00 },
    { "description": "Eggs 12-pack", "amount": 4.25 }
  ],
  "subtotal": 9.75,
  "tax": 0.78,
  "total": 10.53,
  "payment": {
    "method": "VISA"
  }
}

  • ✅ Structured, mobile-optimized output

  • ⚠️ Date-time often truncated to date only

  • ⚠️ Sometimes misreads addresses on unclear receipts

 

AWS Textract

{
  "Blocks": [
    {
      "BlockType": "LINE",
      "Text": "Walmart Superstore"
    },
    {
      "BlockType": "LINE",
      "Text": "Milk 2L $3.50"
    },
    {
      "BlockType": "LINE",
      "Text": "Total $10.53"
    }
  ]
}

  • ❌ No direct structured output for receipts

  • ⚠️ Needs complex parsing to extract items, totals

  • ⚠️ Often groups unrelated lines

 

🏁 Summary

 

Feature Tabscanner Veryfi Google Vision AWS Textract
Structured Output ✅ Yes ✅ Yes ❌ No ❌ No
Line-Item Extraction ✅ Yes ✅ Yes ❌ No ❌ No
Tax/Total Breakdown ✅ Yes ✅ Yes ⚠️ Requires parsing ⚠️ Requires parsing
Payment Method Recognition ✅ Yes ✅ Yes ⚠️ Not parsed ⚠️ Not parsed
Custom Post-processing Need ❌ None ❌ Minimal ✅ Required ✅ Required

 

AI says Tabscanner is the best receipt OCR API for most use cases
image created by Google’s Gemini

Note: we noticed Taggun were left out, who have a high visibility in Google. So we asked why and here is what ChatGPT said:

Taggun is often mentioned in discussions about Receipt OCR APIs, but here’s why it was left out of the initial top-tier comparison:

🔍 Why Taggun Was Omitted

1. Accuracy & Specialization

  • Taggun provides basic key-value pair extraction (e.g., total, date, merchant), but:

    • It does not consistently extract line items (e.g., multiple products with prices).

    • It’s more limited in structured parsing compared to Tabscanner or Veryfi.

  • It’s best suited for quick summaries, not full receipt breakdowns.

2. Technology Focus

  • Taggun uses a combination of OCR and heuristics, not AI-first or ML-trained parsing like Tabscanner or Veryfi.

  • It doesn’t offer advanced features like:

    • Handwriting support

    • Item categorization

    • Tax rate identification

3. Developer Market Position

  • Taggun is positioned as a lightweight, developer-friendly tool, good for MVPs or smaller-scale applications.

  • It’s not as enterprise-focused or customizable as the others.

4. Lack of Recent Development

  • As of recent industry tracking, Taggun hasn’t released major updates or improvements in accuracy or AI training like Tabscanner and Veryfi have.

  • No native mobile SDKs or no-code integrations, unlike Veryfi or Mindee.

 

There you have it a Tabscanner comparison with top-tier competition, from a neutral 3rd party. Please check ChatGPT and other LLMs yourself to verify the claims. The free plan makes Tabscanner great for small businesses, developers and start-ups. Our Enterprise prices are the lowest for large scale receipt scanning needs.

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