在图像中找到矩形并提取其中的文本以将其另存为新图像

Question

我是OpenCV的新手，所以我真的需要您的帮助。我有一堆像这样的图像：

我需要检测图像上的矩形，从其中提取文本部分并将其另存为新图像。

您能帮我吗？

谢谢！

Answer 1

执行此操作的一种方法（如果矩形大小在某种程度上可以预测）是：

1. 将图像转换为黑白
2. 反转图像
3. 使用水平线/矩形（我尝试使用2x30）在（2）的图像上进行形态学开口。
4. 用（2x）的垂直线对图像进行形态学开口（我尝试使用15x2的宽度）。
5. 添加（3）和（4）中的图像。您现在应该只有一个白色矩形。现在可以删除原始图像中所有对应的行和列，这些行和列在该图像中全部为零。

Answer 2

只需添加到Danyals答案中，我已经添加了示例代码，并在注释中编写了步骤。对于此图像，您甚至不需要在图像上执行形态学打开。但是通常建议对图像中的这种噪点进行推荐。干杯！

import cv2
import numpy as np

# Read the image and create a blank mask
img = cv2.imread('napis.jpg')
h,w = img.shape[:2]
mask = np.zeros((h,w), np.uint8)

# Transform to gray colorspace and invert Otsu threshold the image
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# ***OPTIONAL FOR THIS IMAGE

### Perform opening (erosion followed by dilation)
#kernel = np.ones((2,2),np.uint8)
#opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

# ***

# Search for contours, select the biggest and draw it on the mask
_, contours, hierarchy = cv2.findContours(thresh, # if you use opening then change "thresh" to "opening"
                                          cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
cv2.drawContours(mask, [cnt], 0, 255, -1)

# Perform a bitwise operation
res = cv2.bitwise_and(img, img, mask=mask)

########### The result is a ROI with some noise
########### Clearing the noise

# Create a new mask
mask = np.zeros((h,w), np.uint8)

# Transform the resulting image to gray colorspace and Otsu threshold the image 
gray = cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Search for contours and select the biggest one again
_, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)

# Draw it on the new mask and perform a bitwise operation again
cv2.drawContours(mask, [cnt], 0, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)

# If you will use pytesseract it is wise to make an aditional white border
# so that the letters arent on the borders
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(res,(x,y),(x+w,y+h),(255,255,255),1)

# Crop the result
final_image = res[y:y+h+1, x:x+w+1]

# Display the result
cv2.imshow('img', final_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果：