> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Module Introduction
> Overview of information extraction techniques that convert documents, images, audio, and video into structured data and guidance on Azure services, technologies, and solution patterns
Welcome to the final module of this course: Getting Started with Information Extraction.
In this lesson you'll learn how AI converts content that is difficult for machines to interpret—text embedded in documents and images, spoken words in audio, and visual cues in video—into structured data that applications can consume. This module focuses on the core concepts, enabling technologies, practical patterns, and how Microsoft/Azure services map to common information-extraction scenarios.
Information extraction (IE) is the process of turning unstructured or semi-structured content into structured outputs such as entities, key-value pairs, tables, transcripts, and metadata. IE accelerates analytics, automates data capture, reduces manual effort, and speeds decision-making.
## What we'll cover
* A clear definition of information extraction and the problems it solves.
* Core enabling technologies and what each produces (e.g., text, entities, tables, transcripts).
* How Microsoft/Azure offerings map to different content types (documents, images, audio, video).
* How content-understanding capabilities unify multimodal extraction into consistent structured outputs.
* Practical selection guidance and common patterns used in real-world solutions.
## What is information extraction?
Information extraction (IE) is the set of techniques that detect and extract relevant data points from unstructured sources so that downstream systems (analytics, workflows, search, databases) can use them.
Common business goals for IE:
* Automate data capture from invoices, contracts, forms, receipts.
* Convert scanned documents, screenshots, and photos into searchable text.
* Produce transcripts and speaker-attributed text from meetings and calls.
* Extract visual metadata from video for indexing, moderation, and search.
* Reduce manual review time and human error.
## Core enabling technologies
* Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR)
* Recognize printed and handwritten text in scanned documents and images.
* Link: [https://learn.microsoft.com/azure/cognitive-services/computer-vision/overview-ocr](https://learn.microsoft.com/azure/cognitive-services/computer-vision/overview-ocr)
* Document Intelligence (document understanding / parsing)
* Extract entities, key-value pairs, tables, and document structure from forms and documents.
* Link: [https://learn.microsoft.com/azure/applied-ai-services/document-intelligence/overview](https://learn.microsoft.com/azure/applied-ai-services/document-intelligence/overview)
* Note: Historically known in some platforms as Form Recognizer.
* Link: [https://learn.microsoft.com/azure/applied-ai-services/form-recognizer/overview](https://learn.microsoft.com/azure/applied-ai-services/form-recognizer/overview)
* Speech-to-text (automatic speech recognition, ASR)
* Convert spoken audio to text, enabling search, compliance, and analytics.
* Link: [https://learn.microsoft.com/azure/cognitive-services/speech-service/speech-to-text](https://learn.microsoft.com/azure/cognitive-services/speech-service/speech-to-text)
* Video analysis
* Extract transcripts, speaker segments, scene changes, object detection, and visual metadata.
* Link: [https://learn.microsoft.com/azure/azure-video-indexer/overview](https://learn.microsoft.com/azure/azure-video-indexer/overview)
## How these capabilities map to structured outputs
| Content Type | Typical Azure Service(s) | Common Outputs | Typical Use Cases |
| - | - | - | - |
| Scanned documents & images | OCR, Document Intelligence | `text`, `entities`, `key-value pairs`, `tables`, `layout` | Invoices, contracts, receipts, ID processing |
| Handwritten notes | HTR, Document Intelligence | `handwritten text`, `entities` | Forms, survey responses, historical documents |
| Audio (meetings, calls) | Speech-to-text | `transcripts`, `timestamps`, `speaker labels` | Call analytics, compliance, search |
| Video (recordings, surveillance) | Video analysis + OCR/ASR | `transcripts`, `scene/object annotations`, `face/speaker segments` | Media indexing, content moderation, highlight reels |
| Multimodal inputs | Combined services / platform-level content understanding | Unified structured records across text, audio, and visual data | End-to-end document + audio workflows, multimodal search |
## When to use Document Intelligence vs OCR
* Use OCR when you primarily need raw text extraction from images or scans.
* Use Document Intelligence when you need structured outputs (entities, tables, relationships) and higher-level semantic parsing.
* Often both are used together: OCR provides text regions; Document Intelligence extracts semantics on top of that text.
## Practical guidance — choosing the right approach
1. Identify the primary content type(s): document, image, audio, or video.
2. Determine the desired structured outputs: raw text, key-value pairs, tables, entities, transcripts, or visual metadata.
3. Start with out-of-the-box models for speed; move to custom models when domain-specific accuracy is required.
4. Evaluate privacy, PII handling, and compliance requirements early—these influence architecture and on-premise vs cloud choices.
5. Design for feedback loops: integrate human review for low-confidence extractions and use corrected examples to improve models.
Be mindful of privacy and compliance. Extracting personal or sensitive data (PII) requires appropriate consent, data protection controls, and secure storage. Architect workflows to mask or redact sensitive fields when needed.
## Common patterns and solution architecture
* Pipeline pattern: OCR/HTR → Document Intelligence → Post-processing (validation, enrichment) → Structured storage / workflows.
* Hybrid human-in-the-loop: Automated extraction + manual review for low-confidence records.
* Multimodal fusion: Combine text, audio, and visual signals to produce a single, coherent structured representation.
## Learning objectives — what you'll be able to do
* Define information extraction and list common problems it solves.
* Explain how OCR and document intelligence work together to turn unstructured documents into actionable data.
* Identify which Azure services to consider for documents, images, audio, and video.
* Describe how platform-level content-understanding capabilities unify multimodal extraction for consistent outputs.
## Links and references
* [Azure Cognitive Services overview](https://learn.microsoft.com/azure/cognitive-services/)
* [OCR (Computer Vision) overview](https://learn.microsoft.com/azure/cognitive-services/computer-vision/overview-ocr)
* [Document Intelligence (Document Understanding)](https://learn.microsoft.com/azure/applied-ai-services/document-intelligence/overview)
* [Form Recognizer (historical)](https://learn.microsoft.com/azure/applied-ai-services/form-recognizer/overview)
* [Speech-to-text (Speech Service)](https://learn.microsoft.com/azure/cognitive-services/speech-service/speech-to-text)
* [Azure Video Indexer overview](https://learn.microsoft.com/azure/azure-video-indexer/overview)
This module emphasizes concepts and practical patterns so you can design information-extraction solutions that fit real-world AI pipelines.
This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.