Information Extraction: From Chaos to Structure
Documents, images, audio, video — all full of valuable data locked in unstructured formats. Information extraction AI turns chaos into clean, structured, searchable data.
What is information extraction?
Information extraction is AI reading a messy document and pulling out exactly what you need — like a really efficient assistant.
You hand your assistant a stack of 500 invoices. You say: “Get me the supplier name, total amount, and due date from each one.” They’d take weeks. An extraction AI does it in minutes.
But it’s not just documents — AI can extract information from images (photos of receipts), audio (recorded meetings), and video (presentation slides in a webinar).
Extraction across modalities
| Feature | What AI Extracts | Example |
|---|---|---|
| 📄 From text/documents | Specific fields from forms, invoices, contracts, reports | Invoice number, supplier name, line items, total amount |
| 🖼️ From images | Text, objects, labels, and metadata from photos | Product label info, building permit numbers, medical chart readings |
| 🎙️ From audio | Spoken content, speaker identity, key phrases, topics | Meeting action items, interview highlights, customer complaints |
| 🎬 From video | Visual content, spoken words, on-screen text, scenes | Presentation slide text, training video topics, security footage events |
Document extraction
The most common extraction scenario. AI reads structured and semi-structured documents and extracts specific fields.
| Document Type | Fields Extracted |
|---|---|
| Invoices | Invoice number, vendor, date, line items, total, tax |
| Receipts | Store name, items, prices, total, date |
| ID documents | Name, date of birth, document number, nationality |
| Health records | Patient name, diagnosis, medications, dates |
| Contracts | Parties, dates, terms, obligations, amounts |
GreenLeaf scenario: GreenLeaf receives hundreds of supplier invoices per month in different formats — some printed, some scanned, some handwritten. Content Understanding reads each one and extracts the vendor name, amounts, and payment terms into their accounting system.
How extraction differs from text analysis
| Feature | Text Analysis | Information Extraction |
|---|---|---|
| Goal | Understand meaning and sentiment | Pull out specific data fields and values |
| Input | Usually clean text | Documents, images, audio, video (messy/varied) |
| Output | Sentiment scores, keywords, entities, summaries | Structured data: { field: value } pairs |
| Example | 'This review is 85% positive' | 'Invoice #4521, Total: $3,400, Due: 15 May 2026' |
| Azure service | Azure AI Language | Azure Content Understanding |
Azure Content Understanding
Azure Content Understanding is the Azure service for multimodal information extraction. It’s part of Foundry Tools and can process:
- Documents and forms (PDF, images of forms)
- Images (photos, screenshots)
- Audio (recordings, calls)
- Video (presentations, training content)
You’ll work hands-on with Content Understanding in Domain 2 (Modules 24-27).
How Content Understanding works under the hood
Content Understanding combines multiple AI capabilities:
- OCR — reads text from the document/image
- Layout analysis — understands tables, headers, paragraphs, and document structure
- Field extraction — maps specific regions to named fields
- Validation — checks extracted data against expected formats (dates, numbers, etc.)
For audio and video, it adds: 5. Speech recognition — transcribes spoken content 6. Scene detection — identifies key moments in video 7. Slide extraction — captures on-screen text and slides
This multimodal approach means you can build one extraction pipeline that handles documents, images, audio, AND video.
🎬 Video walkthrough
Flashcards
Knowledge Check
MediSpark receives patient intake forms in multiple formats: some typed PDFs, some scanned handwritten forms, some photographed with phones. They need to extract patient name, DOB, and insurance number from all of them. Which Azure service is best suited?
DataFlow Corp records all customer support calls. They want to extract: the customer's account number (spoken), the issue category, and the resolution provided. Which modality of information extraction is this?
🎉 You’ve completed Domain 1! You now understand AI concepts, responsible AI, model types, deployment, and all six workload categories. Domain 2 takes you hands-on — building real AI solutions in Microsoft Foundry.
Next up: Prompting Fundamentals — crafting effective system and user prompts for generative AI models.