Can you try this new attached image for Alphabet "M" ? On Saturday, October 27, 2018 at 12:22:45 PM UTC+5:30, [email protected] wrote: > > I am using Pytesseract to recognise an image for number 5 and I'm stunned > that even after applying various filters like GlaussianBlur and Threshold > and applying dilation and erosion to remove the noise it still not able to > identify the image. > > > I am using Eng Trained data by default. Not sure where I am going wrong. > Do I need to include any other training file here? > > > Filters Tried: > > > 1: cv2.threshold(cv2.GaussianBlur(img, (9, 9), 0), 0, 255, > cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1], > > 2: cv2.threshold(cv2.GaussianBlur(img, (7, 7), 0), 0, 255, > cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1], > > 3: cv2.threshold(cv2.GaussianBlur(img, (5, 5), 0), 0, 255, > cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1], > > 4: cv2.threshold(cv2.medianBlur(img, 5), 0, 255, > cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1], > > 5: cv2.threshold(cv2.medianBlur(img, 3), 0, 255, > cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1], > > 6: cv2.adaptiveThreshold(cv2.GaussianBlur(img, (5, 5), 0), > 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2), > > 7: cv2.adaptiveThreshold(cv2.medianBlur(img, 3), 255, > cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2), > > > > *Training Data:* > > eng.traineddata > > > *Original Image: See Attached* > > > > > >
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