I'm trying to find if a scanned pdf form contains a signature (like making sure a check is signed).
The problem domain:
I will be receiving document packages (multi page pdf's with multiple forms). I have already put together document package classifiers that will check the package for all documents and scale the images to a common size. After that I know where the signatures should be and can scan the area of the document specifically. What I'm looking for is the best approach to making sure there is a signature present. I've considered just checking for a base threshold of dark pixels but that seems so clumsy. The trouble with signatures is that they are not really writing, more of a personal mark.
The only thing I can come up with is a machine learning method to look for loopyness? But I'm not all the familiar with machine learning and don't even know where to start with something like that. Anyone with some suggestions for practical approaches would very appreciated.
I'm coding this in Java if that's helpful at all
What you asked was very broad so there isn't a lot of information that we can give you. However, I can point you to some helpful links:
http://java-ml.sourceforge.net/ --This is a library that you can download that has lots of useful algorithms and other code to include in your program
https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU --this is a series that explains neural networks (something you might want to look into for your machine learning)
"Relative distances from what?" you say. Well this is where the next tip comes in handy: instead of keeping track of the lines, keep track of the tips of the loops and the order of these points. If you then take the distance between all of them (relatively of course which means to set one of the lengths to zero). Along to keeping track of the distances, you should also keep track of the angles. You would calculate the angle ABC by taking the distance between (A,B), (B,C), and (A,C) (A,B, and C being coordinates on the xy plane) which creates a triangle between the points which allows you to use trigonometry to calculate the angle.
(I am assuming that for all of these you are also trying to detect who's signature it is of course because it actually doesn't really complicate things much at all) When trying to match up the signature detected to the stored signatures to see if they are the "same," don't make it to where the distances and angles have to be exact. Give a margin of error (like use a % range above and below). Here is a tip: Make the margin of error rather large. That way if it is written poorly, it will still be detected. This raises the chances of more than one signature being picked up. Luckily, there is a simply solution to this. Just have it run the algorithm again on the signatures that were found but with the margin of error smaller (you of course don't do this manually, the program does it). Continue decreasing the margin of error until you get only one signature remaining.
I am hoping you have ideas already for detecting where the actual signature is but check for the difference in darkness of the pixels of course. Make sure it is pretty continuous. Also take note of the fact that signatures are commonly signed in both black or blue or sometimes red and other fancy colors.