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What AI workload is used to identify handwritten words in Azure?
Get a clear explanation for the AI-900 exam on why Azure AI Vision is the correct AI workload for identifying handwritten words. Understand the role of Optical Character Recognition (OCR) and why Natural Language Processing and Machine Learning Regression are incorrect.
Question
Which type of AI workload matches the following scenario? “Identify handwritten words”
A. Natural Language processing
B. Azure AI Vision
C. Machine Learning (Regression)
D. AI Anomaly detection
Answer
B. Azure AI Vision
Explanation
The correct AI workload for the scenario “Identify handwritten words” is B. Azure AI Vision. This task is a classic example of computer vision, specifically using Optical Character Recognition (OCR).
Understanding Azure AI Vision and OCR
Azure AI Vision is a suite of services designed to analyze the content of images and videos. A key capability within this service is Optical Character Recognition (OCR), which is specifically engineered to detect and extract text from visual inputs. The “Read” API in Azure AI Vision is optimized to recognize both printed and handwritten text in various languages, making it the precise tool for this scenario. The process involves the AI model analyzing the pixels of an image to identify shapes that correspond to letters and numbers, then converting those shapes into machine-readable character strings.
Why Other Options Are Incorrect
- Natural Language Processing (NLP): This workload is concerned with the interpretation, understanding, and generation of human language after it has been converted into text. NLP would be used to analyze the meaning or sentiment of the handwritten words once they have been identified by OCR, but it does not perform the initial visual recognition.
- Machine Learning (Regression): Regression is a type of supervised machine learning used to predict a continuous numerical output, such as a price, temperature, or sales forecast. It is not suitable for classification or recognition tasks like identifying characters in an image.
- AI Anomaly Detection: This workload focuses on identifying rare events or observations that deviate significantly from the majority of the data. It is used for tasks like fraud detection or identifying faulty equipment, not for recognizing text.
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