classification technique

My BE final year project is about sign language recognition. I'm terribly confused in ch开发者_如何学Gooosing the right classification technique for patterns seen in the video of signs generated by a dumb user. I learned neural nets(NN) are better than hidden markov model in several aspects but fine tuning the parameters of NN requires a lot of time. Further, some reports say that Support Vector Machine are better in performance than NN. What do I choose among these alternatives or are there any other better alternatives so that it would be feasible to complete my project within 4-5 months and I could continue with that field in my masters?

Actually the system will be fed with real time video and we intend to recognize the hand postures and spatiotemporal gestures. So, its the entire sentences I'm trying to find.

On the basis of studies till now, I'm making my mind to use 1. Hu moments & eigenspace size functions to represent hand shapes 2. SVM for posture classification & 3. Threshold HMM for spatiotemporal gesture recognition. What would u comment in these decisions?