Extracting entities, tables, and images from documents
5 minutes
5 Questions
Extracting entities, tables, and images from documents is a crucial capability in Azure AI's knowledge mining and information extraction solutions. This process leverages Azure Cognitive Services, particularly Azure Form Recognizer and Azure Cognitive Search, to transform unstructured documents int…Extracting entities, tables, and images from documents is a crucial capability in Azure AI's knowledge mining and information extraction solutions. This process leverages Azure Cognitive Services, particularly Azure Form Recognizer and Azure Cognitive Search, to transform unstructured documents into structured, searchable data.
**Entity Extraction** involves identifying and classifying key information within documents such as names, dates, locations, organizations, and custom entities specific to your domain. Azure Form Recognizer uses pre-built and custom models to detect these entities. The service applies machine learning algorithms to recognize patterns and extract relevant data points from invoices, receipts, business cards, and other document types.
**Table Extraction** enables the identification and parsing of tabular data embedded within documents. Azure Form Recognizer can detect table boundaries, recognize row and column structures, and extract cell contents while maintaining their relationships. This is particularly valuable for processing financial statements, reports, and forms containing structured data layouts.
**Image Extraction** focuses on identifying and extracting visual elements from documents. This includes photographs, diagrams, charts, logos, and signatures. Azure services can extract these images as separate assets, apply OCR to extract text within images, and use Computer Vision capabilities to analyze and describe image contents.
**Implementation Approach:**
1. Use Azure Form Recognizer's Layout API to analyze document structure
2. Deploy pre-built models for common document types or train custom models
3. Integrate with Azure Cognitive Search to create searchable indexes
4. Configure skillsets to enrich extracted data with additional AI capabilities
The extracted information can be stored in Azure Blob Storage and indexed using Azure Cognitive Search, creating a powerful knowledge mining solution. This enables organizations to unlock insights from large document repositories, automate data entry processes, and build intelligent search applications that understand document content at a granular level.
Extracting Entities, Tables, and Images from Documents
Why It Is Important
Extracting entities, tables, and images from documents is a critical skill for AI engineers because it enables automated processing of unstructured data. Organizations deal with massive volumes of documents including invoices, contracts, forms, and reports. Manual extraction is time-consuming and error-prone. Azure AI Document Intelligence (formerly Form Recognizer) automates this process, enabling faster decision-making, improved accuracy, and significant cost savings.
What It Is
This capability refers to using Azure AI services to identify and extract structured information from documents:
Entities: Named elements such as names, dates, locations, organizations, and custom-defined data points within text.
Tables: Structured data arranged in rows and columns, commonly found in invoices, financial statements, and reports.
Images: Visual elements embedded within documents that may contain relevant information or require separate processing.
How It Works
Azure AI Document Intelligence uses machine learning models to analyze documents:
1. Prebuilt Models: Ready-to-use models for common document types like invoices, receipts, ID documents, and business cards. These models extract standard fields such as vendor name, total amount, and line items.
2. Custom Models: Train your own models using labeled sample documents when dealing with unique document formats. You can create composed models by combining multiple custom models.
3. Layout API: Extracts text, tables, selection marks, and document structure. It identifies table boundaries, cell contents, row and column spans.
4. Read API: Performs OCR to extract printed and handwritten text from images and PDFs.
5. Document Analysis: The service returns JSON output containing extracted data with confidence scores, bounding box coordinates, and hierarchical document structure.
Key Components: - Bounding Boxes: Define the location of extracted elements on the page - Confidence Scores: Indicate the reliability of extracted data - Key-Value Pairs: Associate labels with their corresponding values - Spans: Define the position of text within the document
Exam Tips: Answering Questions on Extracting Entities, Tables, and Images
1. Know Your APIs: Understand the difference between Layout API (structure and tables), Read API (OCR), and prebuilt/custom models. Questions often test which API to use for specific scenarios.
2. Prebuilt vs Custom: Use prebuilt models for standard documents (invoices, receipts). Choose custom models when document formats are unique to your organization.
3. Training Requirements: Remember that custom models require a minimum of 5 labeled sample documents for training, though more samples improve accuracy.
4. Confidence Thresholds: Be prepared for questions about handling low-confidence extractions. Implement validation logic when confidence scores fall below acceptable thresholds.
5. Table Extraction Details: The Layout API returns tables with row index, column index, and cell content. Understand how merged cells (row span, column span) are represented.
6. Supported Formats: Know that Document Intelligence supports PDF, JPEG, PNG, BMP, TIFF, and HEIF formats. Maximum file sizes and page limits may appear in questions.
7. Composed Models: Understand that multiple custom models can be combined into a composed model to handle various document types with a single endpoint.
8. SDK and REST: Questions may reference both REST API calls and SDK methods. Know the basic operations: analyze, get results, and model management.
9. Async Operations: Document analysis is asynchronous. You submit a document, receive an operation ID, then poll for results. This pattern frequently appears in exam scenarios.
10. Region and Endpoint: Ensure you understand how to configure the correct endpoint and API key for your Document Intelligence resource.