AI-900 Practice Question: Describe features of computer vision workloads on Azure
A library wants to digitize a collection of old printed books by converting scanned pages into searchable, editable text. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse Image Analysis (which can describe a scene containing text) with OCR (which specifically extracts the text itself), leading them to choose option A when the task requires editable text output.
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 specifically designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For digitizing old printed books, OCR can process scanned pages to produce digital text that can be indexed and edited, directly meeting the library's requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image Analysis (descriptions and tags)
Why it's wrong here
Azure AI Vision Image Analysis provides machine-generated captions and descriptive tags for images, summarizing objects, scenery, and visual themes. However, those tags are not character-level text: they say 'an open antique book on a wooden table' without producing the actual words printed on the page. For digitizing a book, you need a searchable transcription of every sentence, which this option cannot deliver.
- ✓
Optical Character Recognition (OCR)
Why this is correct
Azure AI Vision's Optical Character Recognition (OCR) capability, specifically the Read API, detects printed and handwritten digits, letters, and words and transcribes them into machine-readable text with page structure. It extracts the raw character content and groups words into lines and blocks, making it possible to generate searchable PDFs or run full-text queries. This is precisely what a library requires when converting scanned book pages into editable and searchable text.
- ✗
Object detection
Why it's wrong here
Object detection in Azure AI Vision (or custom models) localizes specific items in an image by drawing bounding boxes and assigning class labels such as 'book', 'chair', or 'person'. It returns coordinates and category names, not the underlying characters or words printed inside those objects. Recognizing the location of a page does not help transcribe or preserve the text content of a book, so it falls short of digitization objectives.
- ✗
Face detection
Why it's wrong here
Face detection is a computer vision task that identifies human faces in images and optionally estimates attributes like age, gender, or pose; it is irrelevant to text-bearing historical books. The service operates on facial features and landmarks, not glyphs or language. Since old printed books may have no faces at all, face detection would return empty results, leaving the book's text completely unreadable and unusable for searching.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
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.
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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