Adrian here, author of PyImageSearch. Thanks for mentioning the blog. If anyone has any questions regarding learning computer vision, please see my reply to "sphix0r" below.
Hey, amazing blogs. Currently working on degraded scanned documents. Are there algorithms distinguishable for documents and natural images?
I am using open cv to process the documents, curious if I am missing out chunk of cv algorithms specially for scanned administrative documents (financial,personal documents)?
I'm not sure what you mean by "algorithms distinguishable for documents and natural images" -- can you elaborate? OpenCV itself doesn't have builtin functionality to take documents and fit them to a pre-defined template, that tends to be part of a specific use-case/niche of computer vision for document processing. The general idea is to take a document a user has filled out and "fit" it to a blank template, where you know exactly where each field is. That way you can exact the information from the document.
"The general idea is to take a document a user has filled out and "fit" it to a blank template" - I agree point to point. However, I am struggling with templatization due to poor quality of the document images. To process those documents (denoise, super resolution, HE - etc. etc.), the OpenCV algorithms are not working good enough, requires a lot of tuning varying with each document.
So, I was wondering if those algorithms work better for natural images (buildings, people, things etc) than document images (text, graphics) and if so, there must exist algorithms to process such documents I am unaware of.
I am using open cv to process the documents, curious if I am missing out chunk of cv algorithms specially for scanned administrative documents (financial,personal documents)?