How to detect text box and its coordinates in Python?-CodePudding

I have the following image:

And essentially, I would like to get the following result while also detecting the text:

My current approach

I am using

The text is Bodego IV (correctly identified by your code).

Update:

Masking the entire dark background:

For masking the entire background, we may use the following stages (after finding top_left and bottom_right):

Crop a ROI with 10% margins from the bounding rectangle we found earlier.
Find median of Blue, Green and Red color channels.
Assume the dark background color is almost solid, and the median is close to the color of the background.
Build a mask of pixels value close to the median color.
Find contours in the mask.
Find contour with the maximum area (filtering noise).
Find bounding rectangle of the largest contour.
Fill the bounding rectangle with black color.

Code sample:

import numpy as np
import cv2

img = cv2.imread('00025.jpg')

top_left = np.array([100, 0], np.int32)  # top_left we found earlier
bottom_right = np.array([562, 114], np.int32)  # bottom_right we found earlier

cols = bottom_right[0] - top_left[0]  # ROI width
rows = bottom_right[1] - top_left[1]  # ROI height

prct10 = np.array([cols//10, rows//10], np.int32)  # About 10% of width and 10% of height.

top_left = np.maximum(top_left - prct10, 0)  # Subtract 10% from top left coordinate, and clip to [0, 0]
bottom_right = np.minimum(bottom_right   prct10, np.array(img.shape)[1::-1]-1)  # Add 10% to bottom right coordinate and clip to [img.shape[1]-1, img.shape[0]-1]

roi = img[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0], :]  # Crop the relevant ROI (with 10% margins from each side).

# Compute the median of B,G,R of ROI - supposed to be the BGR color of the solid background.
# Note: Due to JPEG compression, the background is not completely solid.
med = np.round(np.median(roi, axis=(0,1))).astype(np.int32)

mask = cv2.inRange(roi, np.maximum(med-5, 0), np.minimum(med 5, 255))  # Build a mask of pixels value close to the median color.

# Find contours in the mask
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0]

# Find contour with the maximum area (filtering noise).
c = max(cnts, key=cv2.contourArea)

rect = cv2.boundingRect(c)  # Find bounding rectangle.

cv2.rectangle(roi, rect, (0, 0, 0), -1)

#img[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0], :] = 0  # Fill the area with zeros.
cv2.imshow('mask', mask)  # Show mask (for testing).
cv2.imshow('img', img)  # Show image (for testing).
cv2.waitKey()
cv2.destroyAllWindows()

Mask:

Output: