You could try looking for bright values in HSV colourspace like this:

from PIL import Image

# Load image and convert to HSV
im = Image.open('t6FkL.png').convert('HSV')

# Split channels, just retaining the Value channel
_, _, V = im.split()

# Select pixels where V>220
res = V.point(lambda p: p > 220 and 255)
res.save('result.png')

Here is a maybe more intuitive way of writing the point() function to deal with compound logic:

#!/usr/bin/env python3

from PIL import Image

# Build a linear gradient 0..255
im = Image.linear_gradient('L')

# Save how it looks initially just for debug
im.save('DEBUG-start.png')

# Make all pixels between 180..220 black, leaving others as they were
res = im.point(lambda p: 0 if p>180 and p<220 else p)

# Save result
res.save('result.png')

Here is the start image:

And the processed image:

Here's an approach using morphological operations + contour filtering

• Convert image to grayscale
• Otsu's threshold to obtain a binary image
• Perform morph close to connect words into a single contour
• Dilate to ensure that all bits of text are contained in the contour
• Find contours and filter using contour area
• Remove text by "filling" in the contour rectangle with the background color

I used chrome developer tools to determine the background color of the image which was (222,228,251). If you want to dynamically determine the background color, you could try finding the dominant color using k-means. Here's the result

import cv2

image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15,3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=1)

dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,3))
dilate = cv2.dilate(close, dilate_kernel, iterations=1)

cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area > 800 and area < 15000:
        x,y,w,h = cv2.boundingRect(c)
        cv2.rectangle(image, (x, y), (x + w, y + h), (222,228,251), -1)

cv2.imshow('image', image)
cv2.waitKey()

Thresholding to make a mask of the whiter areas and then inpainting will work for most cases in this image.

img = cv2.imread('ultrasound.png')
mask = cv2.threshold(img, 210, 255, cv2.THRESH_BINARY)[1][:,:,0]
dst = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)

Here's the mask:

Here's the inpainted image:

Notice the thresholding mask is not exact, and includes lighter regions where there are no letters. But more importantly, there is especially an issue if the mask does not include regions that need to be removed, such as the dark shadows of the crosses in the middle. Here's a zoom-in of that region.

The mask is just of the white region, and doesn't cover the dark areas. For problems like this where thresholding will not be enough, the mask can be adjusted manually. Here I take the original crosses in the mask and shift to also cover the shadows, and the inpainting is much better. (Similarly, if needed, the areas that shouldn't be included in the mask can be manually removed)

crosses = mask[235:267,290:320] | mask[233:265,288:318]
mask[235:267,290:318] = crosses
dst = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)

One way to do this is with a technique called inpainting. You can find that in (Python) Skimage at

http://scikit-image.org/docs/dev/api/skimage.restoration.html#inpaint-biharmonic

or in OpenCV at

https://docs.opencv.org/3.0-beta/modules/photo/doc/inpainting.html https://docs.opencv.org/3.4.0/df/d3d/tutorial_py_inpainting.html

Here is the Python Skimage inpainting processing:

kitty image:

watermark image:

The Skimage inpainting requires a binary mask image. So I can convert your watermark to such a mask by:

convert watermark.png -alpha extract -threshold 0 mask.png

mask image:

Here is the Python code:

#!/opt/local/bin/python3.6

import numpy as np
import skimage.io
import skimage.restoration
import skimage.exposure

img = skimage.io.imread('/Users/fred/desktop/kitty.png')
msk = skimage.io.imread('/Users/fred/desktop/mask.png')
msk = skimage.exposure.rescale_intensity(msk, in_range='image', out_range=(0,1))
newimg = skimage.restoration.inpaint_biharmonic(img, msk, multichannel=True)
skimage.io.imsave('/Users/fred/desktop/kitty_inpaint_biharmonic.png', newimg)

Imagemagick does not have an official version of that. But user snibgo on the Imagemagick forum has implemented a custom version he calls 'hole filling' at http://im.snibgo.com/fillholespri.htm. He shows an example at https://www.imagemagick.org/discourse-server/viewtopic.php?f=1&t=28640#p127233.

Furthermore, on the same page, he shows some clever Imagemagick code that does repeated small amounts of resizing. This achieves a somewhat similar result to inpainting. But in general, it will not be quite as good as inpainting. Nevertheless, it does work moderately well for your image.

kitty image:

watermark image:

First, I have to take your watermark image and extract a binary image from it where the text is white and the background black. Then I use it to make the kitty image transparent where the text resides. Then I crop out the area of the text just to make the subsequent processing faster.

convert kitty.png \
\( watermark.png -alpha extract -threshold 0 -negate \) \
-alpha off -compose copy_opacity -composite \
-crop 490x102+235+150 +repage tmp1.png

Then I run his rather long sequence of successive resizing of the image followed by merging all the layers and resizing back to the original size.

convert tmp1.png \
\( +clone -resize 90.9091% \) \
\( +clone -resize 90.9091% \) \
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-layers RemoveDups \
-filter Gaussian -resize 490x102! \
-background None \
-compose DstOver -layers merge \
-alpha opaque \
tmp2.png

Then finally, I composite this result back into the place on the kitty image from which I had cropped it.

convert kitty.png tmp2.png -geometry +235+150 -compose over -composite kitty2.png

At full resolution, you can still make out the very faint text residual in this image. The Skimage result is better as can be seen by rapidly alternating the two images.