python opencv pytesseract 验证码识别的实现

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一、环境配置

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

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

安装好Tesseract-OCR.exe

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 as cv
import pytesseract
from PIL import Image


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 as cv
import pytesseract
from PIL import Image


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 as cv
import pytesseract
from PIL import Image


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：