Discover how Azure AI-900 OCR solutions transform medical record digitization and historical document conversion, essential for mastering AI fundamentals and exam success.
Table of Contents
Question
Which of the following are features of optical character recognition (OCR) solutions? (Choose two.)
A. Counting the number of objects in an image
B. Classifying historical artifacts into categories
C. Digitizing medical records
D. Converting historical documents to text
E. Summarizing an image from a security camera
Answer
C. Digitizing medical records
D. Converting historical documents to text
Explanation
The following two tasks are core features of optical character recognition (OCR):
- Digitization of medical records: In this, scanned documents containing text (e.g., medical records) are converted to editable text formats.
- Converting historical documents to text: This involves converting historical documents (often scanned images) to editable text formats for easier access, preservation, and analysis.
The extraction of text from images is a task for OCR solutions. The challenge of extracting text from images such as road signs, scanned documents, and pictures of whiteboards requires combining computer vision (reading the text) and natural language processing (understanding it). In OCR solutions, training models are used to identify individual characters in images. OCR has a wide range of uses such as automatic mail sorting, digitizing documents, and medical records. The following two tasks are core features of OCR:
- Digitization of medical records: In this task, scanned documents containing text (e.g., medical records) are converted to editable text formats using OCR. This allows for easier searching, editing, and analysis of medical information.
- Converting historical documents to text: This involves converting historical documents (often scanned images) to editable text formats for easier access, preservation, and analysis using OCR technology.
Classifying historical artifacts into categories is a task for image classification, not OCR, which focuses on recognizing text within images.
Summarizing an image from a security camera requires image captioning techniques that analyze the entire image scene, not just text extraction. This is a task better suited for dedicated image analysis solutions.
Counting the number of objects in an image is the functionality of object detection, not OCR, which deals specifically with text recognition.
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