We can reasonably assume it's nothing more complicated than what you can do using a combination of machine learning libraries and OpenCV (however if they have instead some new technique, I hope to find a paper from them in a few months :) ).

EDIT: Adding more details.

If you are looking for similar ideas, you should read papers in the area of object-class segmentation / classification[0][1][2], and generic supervised learning.

[0] https://arxiv.org/abs/1510.03727

[1] https://www.microsoft.com/en-us/research/publication/object-...

[2] https://www.ais.uni-bonn.de/papers/DAGM_NC2_2011_Schulz.pdf

Yup, there aren't any ML built into OpenCV but perhaps they use a ML library on top of OpenCV.

OpenCV doesn't have any ML algorithms builtin that I know of, but the article is pretty vague there eh? Either way, document-scanning from a phone camera is no picnic.

I tried a little while ago. Memory is kind of hazy, but depending on how well you do the image transformation (automatically[ish] skew to rectangle, etc), image quality might get poor. Then you have to do the actual OCR. Now the only complete OSS solution is Tesseract and it's not a state-of-the-art one. There's also ocrpy, but it's more of a toolkit and it's model needs to be trained (one single-line text when I last checked). So yeah, it's fairly hard to do.

Obviously it is a convolutional neural net. Here you can find source code for one of the latest work:

https://github.com/s9xie/hed

Not so obvious. In fact, I believe they are using Random Forest.

Good point

Hi, they are using Random Forest to get the edges :)