Hi,
I am quite new to OpenCV therefore excuse me if I ask silly questions.
I am working on an android project to recognize currency notes to help blind people. I have been using Cascade Classifiers.
It did not give me good results ( maybe something wrong with the negative samples I used )
Then I used ORB algorithm. Here I managed to do a feature matching and recognize the currency note, the issue is there are very high number of false positives. I also read I could use SURF( I did not try it yet). This is a research project, therefore I think I can use SURF but it is also an improved version of SURF and it uses just one image for the matching. That way there could be multiple errors compared to a model trained.
If someone has done this successfully before please let me know, I would like to know the best way to do this and pivot my research and implementation to one method.