Sure, the blog might have missed out on the finer details of the different architectures. We intended to give an overview of some of the techniques used to build such information extraction models, we will definitely dive deeper into one of the architectures/model as a second part to this blog.

Ok, we've changed the title from "Building an ID card reader from scratch with deep learning" to the what the article's title says.

It’s surprising to hear you were getting good results with Python OCR libraries 10 years ago because there aren’t any reliable Python OCR libraries even today! ‘:) Tesseract is very fickle and doesn’t work well in poor lighting conditions (like a nightclub)

What deep learning gives you that’s really useful and valuable (beyond better OCR) is that you can use graph convolutional networks to automatically parse the OCR output and convert it into structured data. You could hand-write a parser or use a template matching approach but you’ll have to create a new parser/template for every ID card type whereas the GCN approach can be used to learn the parser…

the problem with extracting information is not just limited to getting OCR results. the bigger problem while building something like this is extracting the fields and understanding the structure of the document automatically. using some python OCR libraries, you'd probably get text results for a drivers license or a passport separately and process these results on separate rules written for each. with deep learning a non-template solution seems possible which will figure out which ID it is, where the name, address, relevant numbers are and put them in a structure.

The title says "ID card". First you need to read the printed texts so that you can be able to read the chip of ID cards.

This is just an example of a common problem e.g. invoices, transcripts, purchase orders, etc.