一、环境配置

1、需要 pillow 和 pytesseract 这两个库，pip install 安装就好了。

pip install pillow -i http://pypi.douban.com/simple --trusted-host pypi.douban.com

pip install pytesseract -i http://pypi.douban.com/simple --trusted-host pypi.douban.com

2、安装好Tesseract-OCR.exe

3、pytesseract 库的配置：搜索找到pytesseract.py，打开该.py文件，找到 tesseract_cmd，改变它的值为刚才安装 tesseract.exe 的路径。

二、验证码识别

识别验证码，需要先对图像进行预处理，去除会影响识别准确度的线条或噪点，提高识别准确度。

实例1

import cv2 ascv

importpytesseract

from PIL importImage

def recognize_text(image):

    # 边缘保留滤波去噪

    dst= cv.pyrMeanShiftFiltering(image, sp=10, sr=150)

    # 灰度图像

    gray= cv.cvtColor(dst, cv.COLOR_BGR2GRAY)

    # 二值化

    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)

    # 形态学操作   腐蚀膨胀

    erode= cv.erode(binary, None, iterations=2)

    dilate= cv.dilate(erode, None, iterations=1)

    cv.imshow('dilate', dilate)

    # 逻辑运算让背景为白色字体为黑便于识别

    cv.bitwise_not(dilate, dilate)

    cv.imshow('binary-image', dilate)

    # 识别

    test_message= Image.fromarray(dilate)

    text= pytesseract.image_to_string(test_message)

    print(f'识别结果：{text}')

src = cv.imread(r'./test/044.png')

cv.imshow('input image', src)

recognize_text(src)

cv.waitKey(0)

cv.destroyAllWindows()

运行效果如下：

识别结果：3n3D

Process finished with exit code 0

实例2

import cv2 ascv

importpytesseract

from PIL importImage

def recognize_text(image):

    # 边缘保留滤波去噪

    blur=cv.pyrMeanShiftFiltering(image, sp=8, sr=60)

    cv.imshow('dst', blur)

  # 灰度图像

    gray= cv.cvtColor(blur, cv.COLOR_BGR2GRAY)

    # 二值化

    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)

    print(f'二值化自适应阈值：{ret}')

    cv.imshow('binary', binary)

    # 形态学操作获取结构元素开操作

    kernel= cv.getStructuringElement(cv.MORPH_RECT, (3, 2))

    bin1= cv.morphologyEx(binary, cv.MORPH_OPEN, kernel)

    cv.imshow('bin1', bin1)

    kernel= cv.getStructuringElement(cv.MORPH_OPEN, (2, 3))

    bin2= cv.morphologyEx(bin1, cv.MORPH_OPEN, kernel)

    cv.imshow('bin2', bin2)

    # 逻辑运算让背景为白色字体为黑便于识别

    cv.bitwise_not(bin2, bin2)

    cv.imshow('binary-image', bin2)

    # 识别

    test_message= Image.fromarray(bin2)

    text= pytesseract.image_to_string(test_message)

    print(f'识别结果：{text}')

src = cv.imread(r'./test/045.png')

cv.imshow('input image', src)

recognize_text(src)

cv.waitKey(0)

cv.destroyAllWindows()

运行效果如下：

二值化自适应阈值：181.0

识别结果：8A62N1

Process finished with exit code 0

实例3

import cv2 ascv

importpytesseract

from PIL importImage

def recognize_text(image):

    # 边缘保留滤波去噪

    blur= cv.pyrMeanShiftFiltering(image, sp=8, sr=60)

    cv.imshow('dst', blur)

    # 灰度图像

    gray= cv.cvtColor(blur, cv.COLOR_BGR2GRAY)

    # 二值化设置阈值自适应阈值的话黄色的4会提取不出来

    ret, binary = cv.threshold(gray, 185, 255, cv.THRESH_BINARY_INV)

    print(f'二值化设置的阈值：{ret}')

    cv.imshow('binary', binary)

    # 逻辑运算让背景为白色字体为黑便于识别

    cv.bitwise_not(binary, binary)

    cv.imshow('bg_image', binary)

    # 识别

    test_message= Image.fromarray(binary)

    text= pytesseract.image_to_string(test_message)

    print(f'识别结果：{text}')

src = cv.imread(r'./test/045.jpg')

cv.imshow('input image', src)

recognize_text(src)

cv.waitKey(0)

cv.destroyAllWindows()

运行效果如下：

二值化设置的阈值：185.0

识别结果：7364

Process finished with exit code 0