Process the image for good OCR recognition











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I'm trying to proccess a picture. Initially, there is a lot of noise, but I'm trying to make some improvements. Unfortunately, this does not give much result. It may be possible to realize such a thing that somehow select the center of each black line of letters in the word and do something like their skeleton. I have no idea how to do this, so please help here.
The code i'm using now:



word = cv2.resize(word, (word.shape[1]*2, word.shape[0]*2))
kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
word = cv2.filter2D(word, -1, kernel)
word[np.where((word >= [180,180,180]).all(axis=2))] = [255,255,255]
word[np.where((word <= [179,179,179]).all(axis=2))] = [0,0,0]
cv2.imshow(str(i), word)


it gives this result:enter image description here from this: enter image description here










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    down vote

    favorite
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    I'm trying to proccess a picture. Initially, there is a lot of noise, but I'm trying to make some improvements. Unfortunately, this does not give much result. It may be possible to realize such a thing that somehow select the center of each black line of letters in the word and do something like their skeleton. I have no idea how to do this, so please help here.
    The code i'm using now:



    word = cv2.resize(word, (word.shape[1]*2, word.shape[0]*2))
    kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
    word = cv2.filter2D(word, -1, kernel)
    word[np.where((word >= [180,180,180]).all(axis=2))] = [255,255,255]
    word[np.where((word <= [179,179,179]).all(axis=2))] = [0,0,0]
    cv2.imshow(str(i), word)


    it gives this result:enter image description here from this: enter image description here










    share|improve this question


























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      I'm trying to proccess a picture. Initially, there is a lot of noise, but I'm trying to make some improvements. Unfortunately, this does not give much result. It may be possible to realize such a thing that somehow select the center of each black line of letters in the word and do something like their skeleton. I have no idea how to do this, so please help here.
      The code i'm using now:



      word = cv2.resize(word, (word.shape[1]*2, word.shape[0]*2))
      kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
      word = cv2.filter2D(word, -1, kernel)
      word[np.where((word >= [180,180,180]).all(axis=2))] = [255,255,255]
      word[np.where((word <= [179,179,179]).all(axis=2))] = [0,0,0]
      cv2.imshow(str(i), word)


      it gives this result:enter image description here from this: enter image description here










      share|improve this question















      I'm trying to proccess a picture. Initially, there is a lot of noise, but I'm trying to make some improvements. Unfortunately, this does not give much result. It may be possible to realize such a thing that somehow select the center of each black line of letters in the word and do something like their skeleton. I have no idea how to do this, so please help here.
      The code i'm using now:



      word = cv2.resize(word, (word.shape[1]*2, word.shape[0]*2))
      kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
      word = cv2.filter2D(word, -1, kernel)
      word[np.where((word >= [180,180,180]).all(axis=2))] = [255,255,255]
      word[np.where((word <= [179,179,179]).all(axis=2))] = [0,0,0]
      cv2.imshow(str(i), word)


      it gives this result:enter image description here from this: enter image description here







      python image-processing ocr image-recognition cv2






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      edited Nov 8 at 4:44









      Dave W. Smith

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      16k22430










      asked Nov 7 at 15:17









      Никита Беляев

      115




      115
























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          Try cv2.erode().



          cv2.bitwise_not(img,img)
          kernel = np.ones((3,3),np.uint8)
          erosion = cv2.erode(img,kernel,iterations = 1)


          enter image description here






          share|improve this answer





















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            1 Answer
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            active

            oldest

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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes








            up vote
            0
            down vote













            Try cv2.erode().



            cv2.bitwise_not(img,img)
            kernel = np.ones((3,3),np.uint8)
            erosion = cv2.erode(img,kernel,iterations = 1)


            enter image description here






            share|improve this answer

























              up vote
              0
              down vote













              Try cv2.erode().



              cv2.bitwise_not(img,img)
              kernel = np.ones((3,3),np.uint8)
              erosion = cv2.erode(img,kernel,iterations = 1)


              enter image description here






              share|improve this answer























                up vote
                0
                down vote










                up vote
                0
                down vote









                Try cv2.erode().



                cv2.bitwise_not(img,img)
                kernel = np.ones((3,3),np.uint8)
                erosion = cv2.erode(img,kernel,iterations = 1)


                enter image description here






                share|improve this answer












                Try cv2.erode().



                cv2.bitwise_not(img,img)
                kernel = np.ones((3,3),np.uint8)
                erosion = cv2.erode(img,kernel,iterations = 1)


                enter image description here







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 14 at 9:53









                Ha Bom

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