Is there a way to use opencv and python to also identify approximately similar patterns to a given template image apart from the exact match?

Question

enter image description here

I want to detect the crossed out boxes and mark them using bounding Rectangles. So to simplify the problem, I extracted few images and selected 1 image as the template image.

I want to use opencv and python to detect the test images using a suitable method. The template image and the test images are given below. I want to do the detection purely using opencv and python, without using any deep learning or ai classification.

template image in the left

I tried using matchTemplate() but it gives the exact image match.

import cv2
import numpy as np
import matplotlib.pyplot as plt

"""
This script finds a single match for the template image
and saves the match.
"""

# Load the image
_img = cv2.imread('data/wet/6.jpg', cv2.IMREAD_GRAYSCALE)
_temp = _img.copy()

# Load the template image
_template = cv2.imread('data/wet/9.jpg', cv2.IMREAD_GRAYSCALE)
w, h = _template.shape[::-1]

# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

matched = []

for meth in methods:
  _img = _temp.copy()  
  method = eval(meth)

  # Apply template Matching
  res = cv2.matchTemplate(_img,_template,method)
  min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

  # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
  if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
    top_left = min_loc
  else:
    top_left = max_loc
  bottom_right = (top_left[0] + w, top_left[1] + h)

  cv2.rectangle(_img,top_left, bottom_right, 255, 2)
  matched.append(_img)

view_img = matched[1]
plt.imshow(view_img)
plt.show()
cv2.imwrite('data/wet/10.jpg', view_img)

here's the output.

output image from matchTemplate

I also tried using canny edge detection as well, but still the result was same.