从 python 中的图像中移除黑色 background/black 杂散直线

Removing black background/black stray straight lines from an image in python

我正在尝试使用 Python 和 OpenCV 从这张图片中读取文本。

但是,如果这张照片混淆了文本输出并给出了错误的文本,角落会出现黑色背景。

我尝试使用代码在 OpenCV 中使用自适应高斯阈值:

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img=cv.imread(file_path,0)

img = cv.medianBlur(img,5)
ret,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)

th2 =cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,\
        cv.THRESH_BINARY,11,2)

**th3 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,\
        cv.THRESH_BINARY,11,2)**

titles = ['Original Image', 'Global Thresholding (v = 127)',
        'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']

images = [img, th1, th2, th3]

for i in range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()

此代码的输出为AGT_result

如何只提取单词?

这是 Python/OpenCV 中的一种方法。

  • 读取输入
  • 将黑色的角落映射到白色,黑色阈值比您的文本稍暗,以减轻抗锯齿 white-black 过渡
  • 使用morphology close清理虚线边框
  • 保存结果

输入:

import cv2
import numpy as np

img = cv2.imread('text_black_corners.png')


# map the black corners to white   
img2 = img.copy()
img2[np.where((img2 <= [150,150,150]).all(axis=2))] = [255,255,255]

# apply morphology close
kernel = cv2.getStructuringElement(cv2.MORPH_RECT , (3,3))
result = cv2.morphologyEx(img2, cv2.MORPH_CLOSE, kernel)

# write result to disk
cv2.imwrite("text_black_corners_removed.png", result)

# display it
cv2.imshow("img2", img2)
cv2.imshow("result", result)
cv2.waitKey(0)

结果:

作为 ad-hoc 解决方案,我们可以使用 cv2.floodFill 4 次 - 每个角一个:

img = cv.imread(file_path, 0)

rows, cols = img.shape

cv.floodFill(img, None, seedPoint=(0, 0), newVal=255, loDiff=1, upDiff=1)  # Fill the top left corner.
cv.floodFill(img, None, seedPoint=(cols-1, 0), newVal=255, loDiff=1, upDiff=1)  # Fill the top right corner.
cv.floodFill(img, None, seedPoint=(0, rows-1), newVal=255, loDiff=1, upDiff=1)  # Fill the bottop left corner.
cv.floodFill(img, None, seedPoint=(cols-1, rows-1), newVal=255, loDiff=1, upDiff=1)  # Fill the bottom right corner.

cv.medianBlur后的结果: