![]() Remember, the success of pre-processing heavily depends on the quality and nature of the input image. Img = cv2.erode(img, kernel, iterations=1) Img = cv2.dilate(bin_img, kernel, iterations=1) # Perform dilation and erosion to remove some noise # C - It is just a constant which is subtracted from the mean or weightedīin_img = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2) # BLOCK Size - It decides the size of neighbourhood area. # neighbourhood values where weights are a gaussian window. # ADAPTIVE_THRESH_GAUSSIAN_C: threshold value is the weighted sum of # Use adaptive thresholding to convert the image to binary Here's a Python script using OpenCV for pre-processing an image before extracting text with Pytesseract: # Import the necessary libraries Deskewing: If the text in the image is skewed, straightening it can improve OCR results.Dilation and Erosion: These operations can help increase the text size and remove noise.This can help to make the text more distinguishable from the background. Binarization (Thresholding): Binarization is the process of converting an image to black and white. ![]() Noise can be reduced using techniques like Gaussian blur.
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