I need OCR for receipts, invoices, and IDs, and I want to compare vendors on accuracy and pricing

Here’s a practical way to compare OCR vendors for receipts, invoices, and IDs on accuracy + pricing.

1) First, define the document types and success criteria

These three use cases are different, so score them separately:

• Receipts: item lines, totals, tax, merchant, date, currency
• Invoices: vendor name, invoice number, line items, subtotal/tax/total, PO number, due date
• IDs: name, DOB, ID number, expiry, address, photo presence, MRZ/barcode, document type

For each, decide whether you need:

• Raw OCR only
• Structured extraction (key-value + line items)
• Classification / detection (e.g., identify receipt vs invoice vs ID)
• Verification / fraud checks for IDs

2) Vendors to shortlist

Common options:

General OCR / document AI

• Google Document AI
• AWS Textract
• Azure AI Document Intelligence
• ABBYY Vantage / FlexiCapture
• Mindee
• Veryfi
• Nanonets
• Rossum (strong on invoices)
• Klippa
• Kofax

ID-focused vendors

• Jumio
• Onfido
• Stripe Identity
• Persona
• Trulioo
• Mitek

If IDs are a major part of the workload, you’ll usually want a specialized identity vendor, not just OCR.

3) Compare on the metrics that matter

Use a vendor scorecard with:

Accuracy

Measure by field, not just “OCR accuracy”:

• Character accuracy
• Field extraction accuracy
• Line-item accuracy
• Table reconstruction quality
• Document classification accuracy
• Confidence calibration (does low confidence actually mean low correctness?)

For IDs:

• OCR field accuracy
• Document authenticity / liveness / fraud checks
• MRZ and barcode accuracy

Cost

Compare:

• Per page / per document
• Per API call
• Minimum monthly commitments
• Overage pricing
• Extra charges for:
  • Form parsing / key-value extraction
  • Table extraction
  • ID verification
  • Fraud / liveness checks
  • Human review / manual validation

Operational factors

• Latency
• Batch processing
• Async support
• SDK/API quality
• On-prem or private deployment
• Data retention / privacy
• Supported countries/languages
• Human-in-the-loop tooling
• Schema flexibility

4) How to run a fair bake-off

Use a representative test set:

• 100–300 receipts
• 100–300 invoices
• 100–300 IDs
• Include:
  • Crumpled/low-light images
  • Skewed scans
  • Multi-page invoices
  • Low-res mobile photos
  • Different countries, fonts, layouts
  • Handwritten fields if relevant

Score:

• Exact match rate for critical fields
• Normalized match for dates/amounts
• F1 score for line items/entities
• End-to-end pass rate on your downstream workflow

Tip: For invoices/receipts, a vendor can look good on OCR but fail on line items and totals, which are usually the real business value.

5) Pricing patterns you’ll see

Vendors usually price by one of these models:

A. Per page/document

Common for document AI:

• Good for predictable volume
• Sometimes different rates for “standard OCR” vs “document parsing”

B. Per verification

Common for IDs:

• Often includes OCR + authenticity checks
• May charge extra for selfies/liveness or database checks

C. Tiered volume pricing

• Lower unit cost at higher monthly volumes
• Watch for minimum commits

D. Platform + usage

• Base subscription plus API usage
• Common with enterprise vendors

6) Typical vendor positioning

Very broadly:

• Best cloud OCR/document extraction at scale: Google, AWS, Azure
• Best invoice-centric workflows: Rossum, ABBYY, Nanonets, Mindee
• Best ID verification: Jumio, Onfido, Persona, Stripe Identity
• Best customization / workflow automation: ABBYY, Nanonets, Kofax

7) A simple comparison table template

Use this to evaluate each vendor:

Vendor	Receipts accuracy	Invoices accuracy	IDs accuracy	Line items	ID verification	Latency	Pricing model	Estimated cost/month	Notes
Google Document AI							Per page
AWS Textract							Per page
Azure Document Intelligence							Per page
ABBYY							Platform + usage
Mindee							Per doc
Rossum							Platform + usage
Jumio							Per verification
Onfido							Per verification

8) My recommendation

If you want a fast starting point:

• Receipts + invoices: test Google Document AI, AWS Textract, Azure Document Intelligence, ABBYY, and Mindee
• IDs: test Jumio, Onfido, Persona, and Stripe Identity

That gives you a strong, practical bake-off across:

• general OCR
• structured document extraction
• identity verification

9) If you want, I can build you a vendor comparison matrix

I can create one of these next:

1. A shortlist by use case
2. A detailed vendor comparison table with estimated pricing
3. An evaluation checklist and test plan
4. A recommendation based on your volume and countries

If you share:

• monthly document volume
• countries/languages
• whether you need line items
• whether IDs need fraud/liveness checks
• cloud vs on-prem preference

…I can narrow it down and estimate costs more concretely.