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Sign Gesture Recognition using Combined Features of Sum graph and HMM with Decision tree classifier

This work introduces an Decision tree-based method to classify and recognize sign gestures from the video. A sign language is used by deaf and dumb for communicating. The language is also used in Human Computer Interactions. We obtain the frames from video and extract the motion area relevant to the occurrence of gesture. From this segmented motion area, we extract the skin part which represents the hand part of a frame. Once skin segment is detected, the image is converted into binary image and is filtered by morphological erosion filter. Furthermore co-occurrence of 0’s and 1’s are calculated and HMM states are derived from these values. Mean and standard deviation of HMM states and sumgraph are considered as features. The features are classified with the help of decision tree classifier. The work is tested with five distinct signs of numerical one to five as used commonly by us to depict the numbers. Results show that the technique is invariant of the size of the hand or the user but are sensitive to light variations due to dependence of the motion detection on light intensity varying. We have observed that system recognizes the gestures with an efficiency of 83.33%. Keywords— HMM, Sign Gesture and language , Decision Tree classifier, sum Graph.