Hello,
I hope my question is not too off topic but its a least a bit opencv related. In my current task i need to determine which label belongs to which inputfield. I have the boxes of all text and inputs and their type(text box and input box and their type(radio, input, etc..)) available. (I uses opencv dnn module for getting these information and also other sources)
So my idea is now to use linear regression(trained on some collected data) to determine the top left(tl) and bottom right(br) points for a label for a given input element.
Now my question is: Does this approach makes sense? Lienar regression only outputs a single variable from what i remember - do i need to train multiple "regressors"(two for tl, and two for br - sounds a bit strange to me)? Is this solvable as classification problem(i would say no at this point - maybe i am wrong)? Can i do linear regression with opencv(its more a computer vision library - but it has that nice dnn module(even with cuda support now on master!))
I read that most neural networks uses regressors for finding the correct bounding box(instead of using sliding window approach).
Thank you very much, Holger