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






python image-processing ocr image-recognition cv2







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    up vote
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    down vote

    favorite
    1












    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







    share|improve this question


























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      up vote
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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






      python image-processing ocr image-recognition cv2







      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




      python image-processing ocr image-recognition cv2






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      edited Nov 8 at 4:44









      Dave W. Smith

      16k22430




      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

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            active

            oldest

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            active

            oldest

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            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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