c++ - 如何使用 OpenCV 从图像中提取文本行

Question

我正在将 OCR 图像发送到 tesseract，在将其发送到 tesseract 之前，我对其进行了一些预处理。我在图像上设置了一个阈值。

我想使用 OpenCV 以某种方式检测文本行或从中裁剪掉所有白点，因此它看起来像这样: 因为当我将此图像发送到 tesseract 时，它可以完美地读取文本。

问题

• 有哪些方法可以做到这一点？

注意:我已经尝试将阈值从 60% 增加到 90%，但它开始扭曲实际文本，使 tesseract 更难阅读。

Answer 1

编辑

我已经删除了旧的东西，因为它在做不必要的事情而且帖子越来越长

结果

没有解释

import cv2
import numpy as np

img = cv2.imread('c:/data/ocr2.jpg')
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = gray.astype('float32')
gray/=255
dct=cv2.dct(gray)
vr=1.#vertical ratio
hr=.95#horizontal
dct[0:vr*dct.shape[0],0:hr*dct.shape[1]]=0
gray=cv2.idct(dct)
gray=cv2.normalize(gray,-1,0,1,cv2.NORM_MINMAX)
gray*=255
gray=gray.astype('uint8')

gray=cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT,
    cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15)),
    iterations=1)
gray=cv2.morphologyEx(gray, cv2.MORPH_DILATE,
    cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)),
    iterations=1)
gray=cv2.threshold(gray,0,255,cv2.THRESH_OTSU)[1]

contours,hierarchy = cv2.findContours(gray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
boxmask=np.zeros(gray.shape,gray.dtype)
for i in xrange(len(contours)):
    x,y,w,h = cv2.boundingRect(contours[i])
    cv2.rectangle(boxmask,(x,y),(x+w,y+h),color=255,thickness=-1)
cv2.imshow('done',img&cv2.cvtColor(boxmask,cv2.COLOR_GRAY2BGR))
cv2.imwrite('done.jpg',img&cv2.cvtColor(boxmask,cv2.COLOR_GRAY2BGR))
cv2.waitKey(0)

有解释

import cv2
import numpy as np
#import skimage.morphology as smp

'''
prerequisite
* some hands on practice on manipulation of 2d spectrums will make things much easier to grasp
** http://www.jcrystal.com/ 'FTL - SE' can be used to easily try stuff out
** try the phase spectrum filtering there
* dct was used here because it's just simpler to manipulate. dft can also be used to get the same effect

outline
* the main 'aha' was to notice that even afer a very large portion of the orignal specturm was zero/ed out,
  the area of interest fails to completely disappear unlike the rest. so the solution trys to box that part
* also note that the text of interest is always horizontal,so throwing away more vertical components bring it out even more

'''
cv2.namedWindow('img',0)
cv2.namedWindow('dct before',0)
cv2.namedWindow('dct after',0)
cv2.namedWindow('low freq suppressed',0)
cv2.namedWindow('bring out black gaps',0)
cv2.namedWindow('connect them together',0)
cv2.namedWindow('auto thresh',0)
cv2.namedWindow('boxmask',0)
cv2.namedWindow('done',0)


img = cv2.imread('c:/data/ocr2.jpg')
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

orig=gray.copy()
gray = gray.astype('float32')
gray/=255
dct=cv2.dct(gray)

dctvis=cv2.normalize(np.log(dct.copy()),-1,0,1,cv2.NORM_MINMAX)
cv2.imshow('dct before',dctvis)

vr=1.#vertical ratio, how much percentage of vertical freq components should be thrown away
hr=.95#horizontal
dct[0:vr*dct.shape[0],0:hr*dct.shape[1]]=0

dctvis=cv2.normalize(np.sqrt(dct.copy()),-1,0,1,cv2.NORM_MINMAX)
cv2.imshow('dct after',dctvis)
gray=cv2.idct(dct)
gray=cv2.normalize(gray,-1,0,1,cv2.NORM_MINMAX)
gray*=255
gray=gray.astype('uint8')

cv2.imshow('low freq suppressed',gray)
gray=cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT,#smp.disk(7)
    cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15)),
    iterations=1)
cv2.imshow('bring out black gaps',gray)
gray=cv2.morphologyEx(gray, cv2.MORPH_DILATE,#smp.disk(5), 
    cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)),
    iterations=1)
cv2.imshow('connect them together',gray)
gray=cv2.threshold(gray,0,255,cv2.THRESH_OTSU)[1]
cv2.imshow('auto thresh',gray)

contours,hierarchy = cv2.findContours(gray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
boxmask=np.zeros(gray.shape,gray.dtype)
for i in xrange(len(contours)):
    x,y,w,h = cv2.boundingRect(contours[i])
    cv2.rectangle(boxmask,(x,y),(x+w,y+h),color=255,thickness=-1)
cv2.imshow('boxmask',boxmask)
cv2.imshow('done',img&cv2.cvtColor(boxmask,cv2.COLOR_GRAY2BGR))
cv2.waitKey(0)