jumpskiphop

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.

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 article reviews all the recent deep learning based approaches to digitization and OCR along with an explanation of how graph neural networks work and how they can be applied to the problem of ID card digitization.