I'm building an app that needs OCR plus AI extraction from documents

A solid pattern is:

1. OCR: turn scans/PDFs/images into text with coordinates
2. Document understanding: use an LLM to extract structured fields from the OCR text
3. Validation: normalize, score confidence, and flag missing/ambiguous fields
4. Human review: only for low-confidence cases

Recommended architecture

Input

• PDF, image, or multipage scan
• Detect whether it’s:
  • digital PDF with embedded text
  • scanned PDF
  • image-based document

OCR layer

Use one of:

• Google Document AI / Vision OCR
• AWS Textract
• Azure Document Intelligence
• Tesseract for low-cost/local OCR
• PaddleOCR if you want strong open-source OCR

If you need:

• best accuracy on forms/invoices/IDs: cloud document AI tools
• local/private: Tesseract or PaddleOCR, possibly with preprocessing

Extraction layer

Use an LLM to convert OCR output into a schema, for example:

{
  "invoice_number": "",
  "invoice_date": "",
  "vendor_name": "",
  "total": "",
  "line_items": []
}

Best practice:

• give the model the OCR text
• constrain output to JSON
• ask it to return null when unsure
• include field definitions and examples

Post-processing

• Normalize dates, currency, names
• Validate totals, tax math, IDs, etc.
• Reject or flag low-confidence extractions
• Track source spans if possible for traceability

Practical pipeline

Document upload
→ file type detection
→ OCR
→ layout/text cleanup
→ LLM extraction to schema
→ validation rules
→ confidence scoring
→ human review if needed
→ structured output to DB/API

Key design tips

• Preserve page numbers and bounding boxes
• Chunk long OCR text by page/section
• Use structured outputs / JSON schema if your LLM supports it
• Keep the OCR text and extracted JSON for auditability
• If documents are repetitive, consider few-shot examples per template

Common use cases

• Invoices
• Receipts
• Bank statements
• Contracts
• IDs / passports
• Medical forms
• Shipping documents

If you want a fast MVP

A good low-friction stack is:

• OCR: AWS Textract or Google Document AI
• Extraction: GPT with structured JSON output
• Backend: Python/Node
• Review UI: simple web dashboard for exceptions

If you want lower cost / more control

• OCR: PaddleOCR
• Extraction: open-source or hosted LLM
• Add custom rules for validation

If you want, I can help you with one of these next:

1. design a production architecture
2. compare OCR vendors
3. draft a JSON schema for extraction
4. write sample Python/Node code for the pipeline