In principle, you could train a model like this to jointly model the text and the produced handwriting and then search for the most likely text to correspond to handwriting, but it would be a lot less efficient and likely not work as well. Instead, the natural way to apply neural networks to use a convolutional neural network. You could either predict the presence of characters at different positions and stitch them together with another program, or do an end-to-end image to sequence approach, probably using attention.

If you want to visualize that kind of model, the techniques you want to use are pretty different than what we have in this article. But there are some pretty useful techniques! In particular, you could do attention visualization to understand where your model is looking as it predicts particular characters and optimization-based feature visualization to understand what different features in your model represent.