linear detection by considering the thickness of Thickness

Here, we call the pixel corresponding to the letter "foreground pixel" and answer your suggestion.

The "straight line" that can be detected by the Huff transformation is simply "linear array of foreground pixels".

On the other hand, the questioner's "characters written with thick lines" is
It may be a "thick line" for humans, but
When it comes to arranging the pixels on the image, it should not be arranged in a straight line.
Therefore, I don't think Huff conversion is a good solution to this problem.

I don't know what the target image looks like, so I can't say anything about it. "Instead, ""Crowded foreground pixels"" could be a characteristic of the character area?"
According to the comments, there are relatively many kanji characters, so

If non-character figures are drawn with sparse lines of scuskers,
I think I can use the local density of the foreground pixels.

Specific examples of processing include
Scan the entire image in a window of appropriate size, such as template matching, and
Count the number of foreground pixels in a window, and if the number is greater than or equal to 20% of the window area,
You can consider the location to be a text area.

As you commented, the processing shown above tends to be heavy.
If you want to scan an entire image with a width and height of W*H in a w*h window,
Approximately (W*H)*(w*h) pixel references occur.

A common way to speed up window scanning is to: There are ways to abort the calculation or shrink the image:

• Stop the calculation halfway
  • If you reach a certain number while counting the foreground pixels in the window, you can judge it as a character area without having to check the remaining pixels.
  • Stop counting when it is determined to be a character area
• Shrink the image
  • Even if the image is reduced, the proportion and positional relationship of foreground pixels and background pixels will not change
  • Reduce the image and reduce the window by just as much
  • If you multiply the image and window by 1/2 each, the pixel reference will be reduced to 1/16

• If you reach a certain number while counting the foreground pixels in the window, you can judge it as a character area without having to check the remaining pixels.
• Stop counting when it is determined to be a character area

• Even if the image is reduced, the proportion and positional relationship of foreground pixels and background pixels will not change
• Reduce the image and reduce the window by just as much
• If you multiply the image and window by 1/2 each, the pixel reference will be reduced to 1/16

OpenCV Expand/Shrink to crush thin lines before detecting straight lines