AI-900 Practice Question: Describe features of computer vision workloads on Azure
A historical society has scanned hundreds of books printed in the 19th century. They want to convert the scanned images into searchable, editable text. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse OCR with general image analysis capabilities like object detection or classification, not realizing OCR is the specific service for text extraction 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)
Optical Character Recognition (OCR) is the Azure Computer Vision capability designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For the historical society's scanned books, OCR can detect characters and words from the 19th-century prints and output them as digital text, enabling full-text search and editing.
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)
Why this is correct
Optical Character Recognition (OCR) is the correct service because it uses computer vision models to detect printed characters on scanned book pages and convert them into machine-readable, searchable, and editable text. Azure's Read API and Document Intelligence rely on OCR to preserve word order, line breaks, and bounding boxes, making the digitized books fully indexable.
- ✗
Object detection
Why it's wrong here
Object detection is incorrect because it localizes and classifies discrete objects, such as people, vehicles, or furniture, by drawing bounding boxes around them in an image. Applied to a scanned book, it might find illustrations or plates, but it cannot recover or digitize textual characters; its output is box coordinates and labels, not a searchable text stream.
- ✗
Image classification
Why it's wrong here
Image classification is incorrect because it assigns a single label or category to the entire image, such as 'scanned page', 'portrait', or 'map', based on its visual content. It can tell you what type of page the book contains, but it does not segment individual characters or produce the actual words on the page, so the text remains trapped inside the image.
- ✗
Facial detection
Why it's wrong here
Facial detection is incorrect because it specializes in locating human faces and face landmarks, such as eyes, nose, and mouth, typically returning face bounding boxes and attributes like age or emotion. While a historical book might include a portrait plate, facial detection would ignore the vast majority of printed text and cannot extract a single word from the page.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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