5.4. Reflection Checkpoint
Key Takeaways
Before proceeding, ensure you can:
- Select the appropriate visual feature for each scenario (caption, tags, objects, read)
- Use the Read API (OCR) for text extraction—remembering this is Computer Vision, not Language
- Distinguish classification (whole image labels) from object detection (bounding boxes)
- Choose between multiclass (one label) and multilabel (multiple tags) classification
- Apply Custom Vision training requirements (minimum 5, recommended 15+ images per class)
- Understand the Video Indexer pipeline for video analysis
Connecting Forward
Phase 6 shifts from visual input to text input. While OCR extracts text from images (Computer Vision), text analytics works with that text (Language). The same document might flow through both: Vision extracts text → Language analyzes sentiment.
Self-Check Questions
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A manufacturing company wants to automatically identify defective products on an assembly line. They have 100 labeled images of defects across 10 defect categories. Which Custom Vision project type should they use, and why might Object Detection be better than Classification?
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An application receives scanned documents and needs to extract text for further processing. The documents contain both printed and handwritten content. Which Azure AI Vision feature handles this, and how does its output structure (pages → lines → words) differ from simple OCR?
