Simple identification codes denoising python

Variety of codes, the method I provided herein have only noise identification verification code is valid.

First, it is I am going to the original picture 4.png

Specific implementation code

Import tesserocr
 from the PIL Import Image, ImageDraw
 Import Time 

# Image Image.open = ( "IMG / 4_1.png") 
# FH = Open ( "IMG / 1.txt", "W") 
# W, H = image.size 
# image-to-text, test 
# for I in Range (H): 
#      for J in Range (W): 
#          Cl image.getpixel = ((J, I)) 
#          clall = Cl [0] + Cl [. 1] Cl + [2] 
#          # == 0 clall current pixel that is black 
#          IF clall == 0: 
#              fh.write ( "0") 
#          the else: 
#              fh.write ( ". 1")
#      Fh.write ( "\ the n-") 
# fh.close () 

# pictures into black and white bicolor 
DEF Black_White (Image): 
    w, H = image.size
     for i in (H) the Range:
         for J in the Range ( W): 
            Cl = image.getpixel ((J, I)) 
            clall = Cl [0] + Cl [. 1] + Cl [2 ]
             # clall == 0 i.e. the current pixel is black 
            IF clall> * = 155. 3 : # tailored to the specific picture 
                image.putpixel ((J, I), ( 255, 255, 255 ))
             the else :
                image.putpixel ((J, I), (0, 0, 0)) 


# binary array 
T2VAL = {}
 DEF twoValue (Image, G):
     for Y in Range (0, image.size [. 1 ]):
         for X in Range (0, image.size [0]): 
            G = image.getpixel ((X, Y))
             IF G> G: 
                T2VAL [(X, Y)] =. 1
             the else : 
                T2VAL [(X, Y) ] = 0 

# noise 
# the RGB value of a point a, compared with the RBG values of the surrounding eight points, a set value N (0 <N <8) , when the RGB value a and the surrounding eight points when RGB equal to the number smaller than N, the noise this point 
#G: Integer binarization threshold N: Integer noise ratio 0 <N <8 Z: Integer frequency noise 
DEF clearNoise (Image, N, the Z):
     for I in Range (0, the Z): 
        T2VAL [(0 , 0)] =. 1 
        T2VAL [(image.size [0] -. 1, image.size [. 1] -. 1)]. 1 = for X in Range (. 1, image.size [0] -. 1 ):
             for Y in Range (. 1, image.size [. 1] -. 1 ): 
                nearDots = 0 
                L = T2VAL [(X, Y)]
                 IF L == T2VAL [(X -. 1, Y -. 1 )]: 
                    nearDots +. 1 =
                

        if L == t2val[(x - 1,y)]:
                    nearDots += 1
                if L == t2val[(x- 1,y + 1)]:
                    nearDots += 1
                if L == t2val[(x,y - 1)]:
                    nearDots += 1
                if L == t2val[(x,y + 1)]:
                    nearDots += 1
                if L == t2val[(x + 1,y - 1)]:
                    nearDots += 1
                if L == t2val[(x + 1,y)]:
                    nearDots += 1
                if L == t2val[(x + 1,y + 1)]:
                    nearDots += 1
                if nearDots < N:
                    t2val[(x,y)] = 1


def saveImage(filename,size):
    image = Image.new("1",size)
    draw = ImageDraw.Draw(image)
    for x in range(0,size[0]):
        for y in range(0,size[1]):
            draw.point((x,y),t2val[(x,y)])
    image.save(filename)


def start(img_path,save_img_path):
    image = Image.open(img_path)
    black_white(image)
    image = image.convert("L")
    twoValue(image,100)
    clearNoise(image,4,1)
    saveImage(save_img_path,image.size)
    print(tesserocr.file_to_text(save_img_path))


img_path = "img/4.png"
save_img_path = "img/4_1.png"
start(img_path, save_img_path)

After treatment get the following picture 4_1.png

Console output

ziri

However, the above is achieved in the ideal case, for some picture recognition rate is not high

Plus some other post-algorithm to improve the recognition rate.

Simple identification codes denoising python

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