问OpenCV中文档的模式抽取层

EN

我知道这是迟来的答复。但我认为未来的人可以从中得到帮助。

下面是我从上面理解的答案(所有代码都是OpenCV-Pythonv2.4-beta版)：

我把这个当作输入图像。这是一个简单的形象，为了理解。

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First we generate the binary image of the give image by thresholding it at 80% of its intensity and inverting the resulting image.

import cv2
import numpy as np

img = cv2.imread('doc4.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,0.8*gray.max(),255,1)
contours, hier = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

缩影图像：

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We considered simple 8-neighborhood connectivity and performed connected component (contour) analysis of the binary image leading to the segmentation of the textual components.

这是简单的轮廓查找在OpenCV，也称为连接组件标签。，它选择图像中的所有白色斑点(组件)。

contours, hier = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

轮廓：

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For next part of algorithm we use the minimum bounding rectangle of contours.

现在我们在每个检测到的轮廓周围找到包围矩形。然后用小区域删除等高线以删除逗号等。参见以下语句：

Smaller connected patterns were discarded based on the assumption that they may have originated due to noise dependent on image acquisition system and does not in any way contribute to the final layout. Also punctuation marks were neglected using smaller size criterion e.g. comma, full-stop etc.

我们还找到了平均身高，avgh。

height = 0
num = 0
letters = []
ht = []

for (i,cnt) in enumerate(contours):
    (x,y,w,h) = cv2.boundingRect(cnt)
    if w*h<200:
        cv2.drawContours(thresh2,[cnt],0,(0,0,0),-1)
    else:
        cv2.rectangle(thresh2,(x,y),(x+w,y+h),(0,255,0),1)
        height = height + h
        num = num + 1
        letters.append(cnt)
        ht.append(h)

avgh = height/num

因此，在删除所有逗号等之后，围绕选定的矩形绘制绿色矩形：

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At this level we also segregate the fonts based on the height of the bounding rect using avgh (average height) as threshold. Two thresholds are used to classify fonts into three categories - small, normal and large (根据给定的通道方程)。

这里的平均身高是40。因此，如果高度小于26.66 (即40x2/3)，则为small，如果是height>60，则为normal。但是在给定的图像中，所有的高度都在(28,58)之间，所以所有的高度都是正常的。所以你看不出区别。

因此，我只是做了一个小的修改，以便于可视化:小的如果height<30，正常的if 3050。

for (cnt,h) in zip(letters,ht):
    print h
    if h<=30:
        cv2.drawContours(thresh2,[cnt],0,(255,0,0),-1)
    elif 30 < h <= 50:
        cv2.drawContours(thresh2,[cnt],0,(0,255,0),-1)
    else:
        cv2.drawContours(thresh2,[cnt],0,(0,0,255),-1)
cv2.imshow('img',thresh2)
cv2.waitKey(0)
cv2.destroyAllWindows()

现在，您得到的结果是将字母分类为小的、正常的、大的：

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These rectangles were then sorted top-to-bottom and left-to-right order, using 2D point information of leftmost-topmost corner.

这部分我省略了。它只是排序所有的包围，重新开始，他们的最左边-最顶端的角落。