AI-900 Practice Question: The Read API is part of Azure Computer Vision.
A logistics company needs to automatically read shipping labels on packages, which include text printed in various fonts and sizes, as well as handwritten addresses. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Candidates often confuse general image analysis capabilities (like tagging or object detection) with text extraction, not realizing that OCR via the Read API is the dedicated service for reading text from images.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Optical Character Recognition (OCR) via the Read API
The Read API is the correct choice because it is specifically designed for extracting printed and handwritten text from images, handling various fonts, sizes, and styles. This makes it ideal for reading shipping labels that contain both machine-printed text and handwritten addresses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Optical Character Recognition (OCR) via the Read API
Why this is correct
The Read API leverages Optical Character Recognition (OCR) technology to extract the actual text characters, both printed and handwritten, from images. For shipping labels, this means it can transcribe addresses, tracking numbers, and postal codes directly into machine-readable strings, along with bounding-box location for each line and word. Because its OCR engine is designed specifically for text extraction, it is the correct service for reading label content.
- ✗
Dense Captioning
Why it's wrong here
Dense Captioning is a Computer Vision feature that generates natural-language descriptions for each salient region of an image, such as 'a cardboard box on a metal table' or 'a person in a yellow coat'. It does not perform character recognition, so although it may identify parts of the shipping label as a 'label' or 'document', it will not output the individual letters and numbers printed on that label. Thus it would be useless for extracting a shipping address or tracking code.
- ✗
Image Analysis - Object Detection
Why it's wrong here
Object Detection (from Image Analysis) identifies and localizes instances of predefined object categories by drawing bounding boxes and assigning class labels like 'package' or 'truck'. It is not designed to read embedded text: even if the detector finds the shipping label itself, it would only return a class label and coordinates, with no transcription of the characters. Therefore, the company would not be able to get the destination or shipment details in a usable format.
- ✗
Image Analysis - Tagging
Why it's wrong here
Image Tagging returns a collection of high-level keywords or tags that describe the image's visual content, for instance 'mail', 'envelope', or 'stamp', potentially along with confidence scores. Tagging is based on visual object recognition and scene classification, not character recognition, so it would not extract the text on the shipping label. The tags might help classify the image but would never yield the actual recipient address or tracking ID.
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