The Document Intelligence Playbook

From Scanned Pages to Structured JSON: A Visual Guide to Modern Data Extraction.

The Leap from OCR to Intelligent Automation

Then: OCR

Optical Character Recognition was a revolution in digitizing text. It converts images to raw text strings, but that's where it stops.

"123 Main St. Total: $599.99 INV-12345"

It sees text, but understands nothing. Developers are left to write brittle, layout-specific rules to find the data they need.

Now: IDP

Intelligent Document Processing uses AI to understand context, classifying documents and extracting data into structured JSON, ready for any application.

{
  "address": "123 Main St.",
  "invoice_id": "INV-12345",
  "total": 599.99
}

It delivers pre-interpreted, structured data, automating entire workflows.

The Cloud Titans: Managed IDP Platforms

Azure, Google Cloud, and AWS offer powerful, managed "buy" solutions. Here's how they stack up on key features.

Minimum Docs for Custom Training

Lower is better. Google's GenAI approach allows for "zero-shot" extraction, a major advantage for rapid deployment.

The Customization Playbook

  • A

    Azure: The Dual-Model Approach

    Offers both fast 'Template' models for fixed layouts and flexible 'Neural' models for variable ones.

  • G

    Google: The GenAI Powerhouse

    Leverages foundation models for zero-shot extraction, requiring no initial training data.

  • W

    AWS: The Query Master

    Customizes by training 'Adapters' to answer specific, natural language questions about your documents.

The New Challengers

Two powerful alternatives are reshaping the landscape: using general-purpose LLMs directly, or building your own solution with open-source tools.

Direct LLM APIs: The Ultimate Flex

Leverage models like GPT-4o or Gemini to "read" any document. Unmatched flexibility, but watch for higher latency and potential "hallucinations".

Self-Hosted: Maximum Control

Build your own pipeline for total data privacy and the lowest long-term cost at extreme scale. Requires significant ML expertise.

Document Input

→

Open-Source OCR

(Tesseract)

↓

Structured JSON

←

LayoutLM Model

(Fine-Tuned)

The Strategic Playbook: Choosing Your Path

The right choice depends on your documents, volume, and in-house expertise. This framework guides your decision.

The Decision Matrix: Effort vs. Control vs. Cost

This chart visualizes the fundamental trade-offs. Bubble size represents relative long-term cost at scale.

Scenario A: Standard Forms (W-2s)

You have high volumes of fixed-layout documents. Reliability and speed are key.

Recommendation: Managed IDP with a Pre-Built Model.

Scenario B: Variable Invoices

You process invoices from thousands of vendors with unpredictable layouts.

Recommendation: Hybrid Approach (IDP + LLM Fallback).

Scenario C: Unstructured Contracts

You need to extract semantic meaning, not just key-value pairs.

Recommendation: Direct Multimodal LLM API.

Scenario D: Max Data Privacy

Data cannot leave your environment. You have a skilled MLOps team.

Recommendation: Self-Hosted Open-Source Pipeline.