Q:

R imager isoblur: how it computes the boundries? It is possible to build a neural network based on its formula?

I’m trying to make something like Word Spotting in R through images. For now, I’ve been able to put some boundries around the words with imager package and isoblur function:
document=imager::load.image("image.jpg")
plot(document)

document1=document<0.8
plot(document1)

plot(document1)
px=(isoblur(document1,1)>.3)
highlight(px)

Document
Document1
Result
The idea is from this work:
https://cran.r-project.org/web/packages/imager/vignettes/pixsets.html
Isoblur description’s is not very helpful to understand the process behind the function and I am wondering:

What are the calculations behind it?
It is possible to construct a neural network to achive the same result, more or less?

A:

What are the calculations behind it?

Isoblur function passes a Gaussian filter on the image, blurring it and removing some noise.

It is possible to construct a neural network to achieve the same
  result, more or less?

Yes, but it is an overkill. If most of your pictures are similar to the one shown you could segment words with thresholding and select each connected component, you should take a look at Otsu method.
If you aim to recognize each character, I think it is better to use an already established OCR tool, such as Tesseract. But your characters seem to be far from the usual handwriting, you probably will have to train your own classifier (might be a good choice to use a neural network for this task).

