验证码多种多样，我这里提供的方法仅对有噪点的验证码进行识别有效。

 

首先，这是我准备的原始图片 4.png

 

具体的实现代码

import tesserocrfrom PIL import Image, ImageDrawimport time# image = Image.open("img/4_1.png")# fh = open("img/1.txt", "w")# w, h = image.size# 图片转文本，测试用# for i in range(h):#     for j in range(w):#         cl = image.getpixel((j, i))#         clall = cl[0] + cl[1] + cl[2]#         # clall == 0即当前像素为黑色#         if clall == 0:#             fh.write("0")#         else:#             fh.write("1")#     fh.write("\n")# fh.close()# 将图片转为黑白二色def black_white(image):    w, h = image.size    for i in range(h):        for j in range(w):            cl = image.getpixel((j, i))            clall = cl[0] + cl[1] + cl[2]            # clall == 0即当前像素为黑色            if clall >= 155*3:  # 根据具体的图片修改                image.putpixel((j, i), (255, 255, 255))            else:                image.putpixel((j, i), (0, 0, 0))#二值数组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            else:                t2val[(x,y)] = 0# 降噪# 根据一个点A的RGB值，与周围的8个点的RBG值比较，设定一个值N（0 
     
      <8），当A的RGB值与周围8个点的RGB相等数小于N时，此点为噪点# G: Integer 图像二值化阀值 N: Integer 降噪率 0 
      
       <8 Z: Integer 降噪次数def clearNoise(image,N,Z):    for i in range(0,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)] = 1def 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)
      
     

 

 

 经过处理后得到以下图片 4_1.png

 

 

控制台输出结果

ziri

 

不过以上是在理想情况下的实现，对于某些图片的识别率不高

 

等后期加上一些算法提高识别率把。