My research is in the area of Document Image Analysis. To be specific, the topic of my thesis is to automatically recognize and index characters in a set of hand-drawn objects, e.g. given a volume of comics --> how to localize, recognize then index the characters in it (e.g. return "Lucky Luke" position in some random volume).
First thing first, before I can work on the indexing part, I must have some reliable system to recognize the interested characters. After some months working on it, I find the recognition is extremely hard, because the drawings are some time very "abstract".
For normal face recognition, there are a lot of researches on it, thus we have Haar-like features, biometrics features, etc. For normal photograph objects, we may use e.g. SIFT/SURF, or other strong features for the purpose of object recognition.
But in cartoons or comics, we may extract so few or zero feature(s) following the traditional approaches. Human visual interpretation system is amazingly effective in recognize such abstract objects (e.g. only with 4 lines and 2 curves, we still recognize the familiar character).
So I think the solution must be somewhere in the understanding how human can interpret such a set of line strokes as a meaningful object. Once we understand it we can try to "teach" the computer the same way.
Thus are there some good approaches in the terms of recognition of abstract objects such as drawn comic objects? (I searched for some but they're not so relevant). Thanks.