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

find rectangle in image and extract text inside of it to save it as new image

我是 OpenCV 的新手,所以我真的需要你的帮助。我有一堆这样的图片:

我需要检测图像上的矩形,从中提取文本部分并将其另存为新图像。

你能帮我解决这个问题吗?

谢谢!

一种方法(如果矩形大小可以预测)是:

  1. 将图像转换为黑白图像
  2. 反转图像
  3. 使用水平线/矩形(我尝试使用 2x30)对 (2) 中的图像执行形态学打开。
  4. 对(2)中的图像用垂直线进行形态学开运算(我用15x2试过)。
  5. 添加 (3) 和 (4) 中的图像。你现在应该只有一个白色的矩形。现在可以删除原始图像中所有相应的行和列,这些行和列在该图像中完全为零。

为了添加到 Danyals 的回答中,我添加了一个示例代码,其中的步骤写在注释中。对于此图像,您甚至不需要对图像执行形态学开运算。但通常对于图像中的这种噪点是推荐的。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: