From what I can tell (without having read the research papers) it looks like this is just an easy to use package for sparse scene text extraction. It seems to do okay if the scene has sparse text but it falls down for dense text detection. The results are going to be pretty bad if you try and do a task like "extract transactions from a picture of a receipt." Here's an example of input you might get for a production app: https://www.clusin.com/walmart-receipt.jpg

Notice the faded text from the printer running out of ink and the slanted text. From limited experience each of these are thorny problems and the state of the art CV algorithms won't help you escape from having to learn how to algorithmicly pre-process images and clean them up prior to feeding them into a CV algorithm. You might be able to use Google's Cloud OCR but that charges per image, although it is pretty good. Even if you use that you've graduated to the next super difficult problem which is Natural Language Processing.

Once you have the text you need to determine if it has meaning to your application. That's basically what NLP is about. For the receipts example, how do you know you're looking at a receipt? What if its a receipt on top of a pile of other receipts? How do you extract transactions from the receipt? Does a transaction span multiple lines? How can you tell? etc etc etc.