Explanation:
Text extraction.
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure AI Vision (formerly Computer Vision), text extraction-also known as Optical Character Recognition (OCR)-is the computer vision capability that detects and extracts printed or handwritten text from images and video frames.
In this scenario, a traffic monitoring system collects vehicle registration numbers (license plates) from CCTV footage. These registration numbers are alphanumeric text that must be read and converted into digital form for processing, storage, or analysis. The Azure AI Vision service's OCR (text extraction) feature performs this function. It analyzes each frame from the video feed, detects text regions (the license plates), and converts the visual text into machine-readable text data.
This process is widely used in Automatic Number Plate Recognition (ANPR) systems that support law enforcement, toll booths, and parking management solutions. The OCR model can handle variations in font, lighting, and angle to accurately extract license plate numbers.
The other options describe different vision capabilities:
* Image classification assigns an image to a general category (e.g., "car," "truck," or "bike"), not text extraction.
* Object detection identifies and locates objects in images using bounding boxes (e.g., detecting the car itself), but not the text written on the car.
* Spatial analysis tracks people or objects in a defined physical space (e.g., counting individuals entering a building), not reading text.
Therefore, for a traffic monitoring system that identifies vehicle registration numbers from CCTV footage, the most accurate Azure AI Vision capability is Text extraction (OCR).