How do I set up a form recognition API for extracting fields from scanned PDFs and uploaded images?

You can set this up with a document AI / form recognition service that can read both scanned PDFs and images and return structured fields.

High-level setup

1. Choose a form recognition service

Common options:

• Azure AI Document Intelligence (great for forms/invoices/receipts/custom extraction)
• Google Document AI
• AWS Textract
• Open-source OCR + model pipeline if you want full control

If you want the simplest “upload file → get fields back” workflow, Azure AI Document Intelligence or AWS Textract are usually the fastest to implement.

---

2. Define what fields you want

Make a list of the fields you need, for example:

• Name
• Date of birth
• Invoice number
• Total amount
• Address
• Signature present

For each field, decide:

• required vs optional
• expected type: text, number, date, checkbox
• whether it appears in the same position every time

This determines whether you should use:

• prebuilt model for common documents
• custom trained model for your own form layout

---

3. Set up file upload

Your API should accept:

• PDF
• JPG / PNG / TIFF

Typical flow:

1. Client uploads file
2. API stores it temporarily or in object storage
3. API sends file to form recognition service
4. Service returns extracted text/fields
5. Your API maps result into your response schema

---

4. Use a prebuilt or custom model

Option A: Prebuilt model

Use this when your docs are standard:

• invoices
• receipts
• identity docs
• tax forms

Pros:

• no training
• fast setup

Option B: Custom model

Use this when you have your own form layout.

Typical steps:

1. Collect 5–50 sample filled forms
2. Label fields
3. Train the model
4. Test on new documents
5. Iterate if accuracy is low

---

5. Build the API endpoint

A common endpoint design:

POST /extract-form

• accepts multipart upload or file URL
• returns extracted fields in JSON

Example request:

POST /extract-form
Content-Type: multipart/form-data

Example response:

{
  "documentType": "application/pdf",
  "fields": {
    "fullName": "Jane Doe",
    "dob": "1990-04-12",
    "policyNumber": "ABC12345",
    "total": "120.50"
  },
  "confidence": {
    "fullName": 0.98,
    "dob": 0.92,
    "policyNumber": 0.99,
    "total": 0.95
  }
}

---

6. Handle OCR and extraction output

Most services return:

• full text
• bounding boxes
• confidence scores
• key-value pairs
• tables

You should:

• normalize dates and amounts
• map service output to your field names
• handle missing/low-confidence fields
• optionally send low-confidence docs for manual review

---

7. Add validation

After extraction, validate:

• required fields present
• dates parse correctly
• numeric fields are numeric
• totals match line items if applicable

This improves reliability a lot.

---

8. Secure the system

Since forms often contain sensitive data:

• use HTTPS
• encrypt files at rest
• delete temporary uploads after processing
• restrict API keys
• log access carefully
• consider data retention policies

---

9. Test with real scans

Use:

• clean scans
• skewed scans
• low-resolution images
• photos taken by phone
• rotated pages
• multi-page PDFs

This will show how robust the system is.

---

Example architecture

1. Frontend uploads scanned PDF/image
2. Backend stores file in S3/Azure Blob/GCS
3. Backend calls form recognition API
4. API returns extracted values
5. Backend validates and stores results
6. Frontend displays extracted fields for review

---

If you want a quick starter choice

• Azure AI Document Intelligence: excellent for custom forms and document extraction
• AWS Textract: strong OCR/key-value extraction
• Google Document AI: strong for structured document processing

If you want, I can give you:

1. a step-by-step setup for Azure/AWS/Google, or
2. a sample API implementation in Python or Node.js.