A completely different answer:
I am currently following a course Computer Vision and Image Analysis: https://courses.edx.org/courses/course-v1:Microsoft+DEV290x+1T2020/course/
With your problem in mind you could follow along. Depending on previous knowledge you could skip a few sections. (I skipped immediately to Beyond Classification/Object Detection)
Globally, you could train an image classification model that could recognize regions of interest, with a classification of the content. Where the course addresses people, cars, buses, in your problem you have images, labels, content (of various types). You may need to experiment, and maybe have 'label-value-pair' as a class, or even 'label-multiline'.
Or maybe labels and values separately work better? Or even a third option that you identify all combinations 'label' 'value' 'label-value'
You should probably define a low-wide boundary box for horizontal label/value pairs, and a more square one for vertical aligned label/value pairs.
You should end up with labeled regions of interest.
If you are happy with these regions, then the second step could be OCR. For the OCR you could use a similar problem division to recognize separate characters, and label the separate characters. And then you still have to combine the characters to words or values.