AI-102 Implement computer vision solutions Practice Question
A retail company uses Azure Computer Vision to analyze customer traffic in stores. They process images from security cameras using the OCR API to detect product labels. Recently, the OCR accuracy has decreased for images with poor lighting. Which pre-processing step should the company implement to improve OCR accuracy?
⚠ Common exam trap
Watch out — candidates often confuse image quality improvements (like resolution or noise reduction) with the specific need to correct lighting-induced contrast loss, which is a distinct pre-processing requirement for OCR in poor illumination.
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
✓
Adjust brightness and contrast of images using image processing.
Poor lighting directly reduces the contrast between text and background, which is critical for OCR accuracy. Adjusting brightness and contrast improves the signal-to-noise ratio of the text regions, making character edges more distinct for the Azure Computer Vision OCR engine. This pre-processing step compensates for the lighting deficiency without altering the fundamental image content that the API relies on.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert images to grayscale before sending to OCR API.
Why it's wrong here
OCR API already handles grayscale conversion internally.
- ✗
Increase the image resolution before calling OCR API.
Why it's wrong here
Higher resolution does not address lighting issues; may increase processing time.
- ✓
Adjust brightness and contrast of images using image processing.
Why this is correct
Improves visibility of text in low-light conditions, enhancing OCR accuracy.
- ✗
Reduce image size to decrease noise.
Why it's wrong here
Smaller images may lose text details, harming OCR accuracy.
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