I'm building a claims workflow and need OCR plus data extraction from scanned PDFs, what should I choose?

For a claims workflow, you usually want OCR + document understanding + a structured extraction layer, not just raw OCR.

What to choose

Best default choice: a cloud document AI service

Pick a service that can do:

• OCR on scanned PDFs
• Form/key-value extraction
• Table extraction
• Layout-aware parsing
• Confidence scores / bounding boxes
• Human review integration

Common strong options:

• Azure AI Document Intelligence
• Google Document AI
• AWS Textract

If you want the quickest path to production for claims docs, these are usually the right starting point.

How to decide

Choose Azure AI Document Intelligence if:

• You’re already on Microsoft/Azure
• You want strong support for forms and custom extraction
• You need a good balance of OCR, templates, and custom models
• You may later integrate with Power Platform or Microsoft workflows

Choose Google Document AI if:

• You need very strong document parsing and layout understanding
• You’re processing diverse document types
• You want strong general-purpose doc extraction and classification

Choose AWS Textract if:

• You’re already on AWS
• You need solid OCR + forms/tables extraction
• You want easy integration with S3/Lambda/Step Functions

For claims specifically

Claims workflows often deal with:

• FNOL forms
• ACORD forms
• medical bills
• repair estimates
• police reports
• invoices/receipts
• scanned attachments of varying quality

So you’ll want:

1. Document classification
   Identify document type first.
2. OCR / text extraction
   Convert scanned pages to text.
3. Field extraction
   Pull out policy number, claim number, date of loss, claimant info, provider info, amounts, etc.
4. Confidence and validation rules
   Catch missing or suspicious values.
5. Human-in-the-loop review
   Route low-confidence docs for manual correction.

Recommended architecture

A practical setup is:

• Ingest PDF
• Split / preprocess pages
• OCR + layout extraction
• Classify document type
• Run type-specific extraction
• Post-process with rules
• Send uncertain fields to review
• Store structured JSON in your claims system

If you need custom fields

If your claims docs have special fields not covered by out-of-the-box models, choose a platform that supports custom model training. Azure and Google both do this well.

If you want to self-host

Only consider open-source OCR if:

• data residency/security rules are strict
• you need full on-prem control
• cost at very high volume is the main driver

Typical stack:

• Tesseract, PaddleOCR, or docTR
• plus custom extraction logic

But for claims, self-hosted OCR usually takes more engineering and tuning.

My short recommendation

• Start with Azure AI Document Intelligence or Google Document AI
• Add human review for low-confidence cases
• Use custom extraction models for your highest-value claim documents
• Only go open-source/on-prem if compliance or cost forces it

If you want, I can also give you:

1. a side-by-side comparison table of Azure vs Google vs AWS for claims, or
2. a reference architecture for a claims OCR/extraction pipeline.