Selecting services for information extraction solutions
5 minutes
5 Questions
Information extraction in Azure AI involves selecting appropriate services based on your specific requirements for extracting structured data from unstructured sources. Azure provides several cognitive services tailored for different extraction scenarios.
Azure AI Document Intelligence (formerly FâŚInformation extraction in Azure AI involves selecting appropriate services based on your specific requirements for extracting structured data from unstructured sources. Azure provides several cognitive services tailored for different extraction scenarios.
Azure AI Document Intelligence (formerly Form Recognizer) is ideal for extracting text, key-value pairs, tables, and structures from documents like invoices, receipts, and forms. It offers prebuilt models for common document types and custom models for specialized needs.
Azure AI Language service provides named entity recognition (NER) for identifying entities such as people, organizations, locations, and dates within text. It also offers key phrase extraction to identify main concepts and sentiment analysis for understanding emotional tone.
Azure AI Vision enables optical character recognition (OCR) for extracting printed and handwritten text from images. This service is essential when dealing with scanned documents or photographs containing textual information.
Azure Cognitive Search combines AI enrichment capabilities with powerful search functionality. It can extract information through built-in cognitive skills including entity recognition, language detection, and image analysis during the indexing process.
When selecting services, consider these factors: the type of source data (documents, images, or text), the structured output format required, accuracy requirements, processing volume, and cost considerations. For complex scenarios, you might combine multiple services in a pipeline.
For example, processing insurance claims might involve Azure AI Vision for OCR on scanned forms, Azure AI Document Intelligence for structured data extraction, and Azure AI Language for sentiment analysis of customer descriptions.
Additionally, evaluate whether prebuilt models meet your needs or if custom model training is necessary for domain-specific terminology. Consider latency requirements for real-time versus batch processing scenarios, and ensure compliance with data residency and security requirements when handling sensitive information.
Selecting Services for Information Extraction Solutions
Why It Is Important
Information extraction is a critical capability in modern AI solutions, enabling organizations to automatically pull structured data from unstructured sources like documents, forms, images, and text. Selecting the right Azure AI service for your specific extraction needs ensures optimal accuracy, cost-efficiency, and performance. For the AI-102 exam, understanding which service to choose for different scenarios is essential, as Microsoft frequently tests your ability to match business requirements with appropriate services.
What It Is
Azure provides several AI services designed for information extraction, each optimized for different use cases:
Azure AI Document Intelligence (formerly Form Recognizer) - Extracts text, key-value pairs, tables, and structure from documents and forms. Supports prebuilt models for invoices, receipts, IDs, and business cards, plus custom model training.
Azure AI Vision (Computer Vision) - Extracts text from images using OCR capabilities. Best for general image-based text extraction and analyzing visual content.
Azure AI Language - Extracts entities, key phrases, and relationships from text. Includes Named Entity Recognition (NER), key phrase extraction, and custom entity extraction.
Azure OpenAI Service - Uses large language models for complex extraction tasks requiring reasoning and context understanding.
How It Works
Each service operates through REST APIs or SDKs:
1. Document Intelligence analyzes document layout, identifies fields, and returns structured JSON with extracted data and confidence scores.
2. AI Vision OCR processes images and returns recognized text with bounding box coordinates.
3. AI Language processes text input and returns identified entities, phrases, or custom-defined information.
The selection process involves matching your input type (documents, images, or text) with the output requirements (structured fields, entities, or raw text).
How to Answer Exam Questions
When facing scenario-based questions, follow this decision framework:
1. Identify the input source - Is it a document, form, image, or plain text?
2. Determine the extraction goal - Do you need key-value pairs, tables, entities, or raw text?
3. Consider customization needs - Does the scenario require prebuilt or custom models?
4. Evaluate scale and integration - What are the processing volume and integration requirements?
Exam Tips: Answering Questions on Selecting Services for Information Extraction Solutions
Tip 1: If the question mentions invoices, receipts, business cards, or ID documents, choose Document Intelligence with prebuilt models.
Tip 2: For custom forms with unique layouts, select Document Intelligence with custom models.
Tip 3: When extracting named entities (people, locations, organizations) from text, choose Azure AI Language NER.
Tip 4: For simple text extraction from images or scanned documents, Azure AI Vision Read API is appropriate.
Tip 5: Look for keywords in questions: 'forms' and 'documents' suggest Document Intelligence; 'entities' and 'phrases' suggest Language service; 'images' suggest Vision service.
Tip 6: Remember that Document Intelligence includes OCR capabilities, so for document scenarios requiring both text extraction and structure analysis, it handles everything in one service.
Tip 7: Cost optimization questions often point toward using prebuilt models over custom models when standard document types are involved.
Tip 8: For scenarios requiring extraction of relationships between entities in text, Azure AI Language with custom models is the appropriate choice.