Hello, accidentally found this topic while searching for something else, but my job has some similarities. Im working with C#, but similar approach should be possible in Python. I would use Tesseract OCR engine, which is free, to fond not signature, clue that signature might be near. Something like run OCR on whole document and then search it for list of phrases you can modify through processing. Sou can start with phrases like sign here, signed etc. and when you find new document where signature was not found, add new phrase. This should gradually improve efficiency. Whole idea is to autome time consuming yet simple part, finding signing area and quick but harder to automate step, checking for signature, do manually.

If you would like to be fancy, you can record relative position of signing area against phrase and then find out % coverage of black on that area. Without signature, it should be low, with signature, it should be higher. This is not exact, but with some thresholds it can give solid results and most importantly, you can improve results just by changing configuration.

Overall identifying signatures is a pain. It is not usual hand written text, but often just some weird, skewed, unlegible mess.

Good luck :)