Python验证码识别处理实例
一、准备工作与代码实例
(1)安装PIL：下载后是一个exe，直接双击安装，它会自动安装到C:\Python27\Lib\site-packages中去，
(2)pytesser：下载解压后直接放C:\Python27\Lib\site-packages(根据你安装的Python路径而不同)，同时，新建一个pytheeer.pth,内容就写pytesser，注意这里的内容一定要和pytesser这个文件夹同名，意思就是pytesser文件夹，pytesser.pth,及内容都要一样！
(3)Tesseract OCR engine下载：下载后解压，tessdata文件夹，用其替换掉pytesser解压后的tessdata文件夹即可。
二、验证
（1）原理：
验证码图像处理
验证码图像识别技术主要是操作图片内的像素点,通过对图片的像素点进行一系列的操作,最后输出验证码图像内的每个字符的文本矩阵。
- 1、读取图片
- 2、图片降噪
- 3、图片切割
- 4、图像文本输出
（2）验证字符识别
验证码内的字符识别主要以机器学习的分类算法来完成,目前我所利用的字符识别的算法为KNN(K邻近算法)和SVM (支持向量机算法),后面我 会对这两个算法的适用场景进行详细描述。
- 1、获取字符矩阵
- 2、矩阵进入分类算法
- 3、输出结果
要验证的图片如下：
（3）、简单的命令：
from pytesser import *
image = Image.open('1.jpg') # Open image object using PIL
print image_to_string(image) # Run tesseract.exe on image
然后运行：
或者直接：
print image_file_to_string('fnord.tif')
同样能输出结果！
（4）、复杂一点的
上面的只能对一些比较简单的做处理，一
原理：彩色转灰度，灰度转二值，二值图像识别
# 验证码识别，此程序只能识别数据验证码
import Image
import ImageEnhance
import ImageFilter
import sys
from pytesser import *
# 二值化
threshold = 140
table = []
for i in range(256):
if i < threshold:
table.append(0)
else:
table.append(1)
#由于都是数字
#对于识别成字母的 采用该表进行修正
rep={'O':'0',
'I':'1','L':'1',
'Z':'2',
'S':'8'
};
def getverify1(name):
#打开图片
im = Image.open(name)
#转化到灰度图
imgry = im.convert('L')
#保存图像
imgry.save('g'+name)
#二值化，采用阈值分割法，threshold为分割点
out = imgry.point(table,'1')
out.save('b'+name)
#识别
text = image_to_string(out)
#识别对吗
text = text.strip()
text = text.upper();
for r in rep:
text = text.replace(r,rep[r])
#out.save(text+'.jpg')
print text
return text
getverify1('1.jpg') #注意这里的图片要和此文件在同一个目录，要不就传绝对路径也行
运行后效果：
以上就是本文的全部内容，希望对大家的学习有所帮助。
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