python
15 lines · 1 tab
Dr. Elena Vasquez
Apr 2026
1 tab
import cv2
image = cv2.imread('receipt.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresholded = cv2.adaptiveThreshold(
blurred,
255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
11,
2,
)
cv2.imwrite('receipt_processed.png', thresholded)
1 file · python
Explain with highlit
A lot of computer vision performance comes from cleaner inputs rather than larger models. I use OpenCV for resizing, denoising, thresholding, and contour extraction when preparing images for OCR or downstream classification. These classical steps often save compute and improve stability.
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