Automatic Indian Sign Language Recognition for Continuous Video Sequence

Sign Language Recognition has become the active area of research nowadays. This paper describes a novel approach towards a system to recognize the different alphabets of Indian Sign Language in video sequence automatically. The proposed system comprises of four major modules: Data Acquisition, Pre-processing, Feature Extraction and Classification. Pre-processing stage involves Skin Filtering and histogram matching after which Eigen vector based Feature Extraction and Eigen value weighted Euclidean distance based Classification Technique was used. 24 different alphabets were considered in this paper where 96% recognition rate was obtained.Keywords: Eigen value, Eigen vector, Euclidean Distance (ED),Human Computer Interaction, Indian Sign Language (ISL), Skin Filtering.Cite as:Joyeeta Singh, Karen Das "Automatic Indian Sign Language Recognition for Continuous Video Sequence", ADBU J.Engg.Tech., 2(1)(2015) 0021105(5pp

Self co‐articulation detection and trajectory guided recognition for dynamic hand gestures

Hand gestures are a natural way of communication among humans in everyday life. Presence of spatiotemporal variations and unwanted movements within a gesture called self co‐articulation makes the segmentation a challenging task. The study reveals that the self co‐articulation may be used as one of the feature to enhance the performance of hand gesture recognition system. It was detected from the gesture trajectory by addition of speed information along with the pause in the gesture spotting phase. Moreover, a new set of novel features in the feature extraction stage was used such as position of the hand, self co‐articulated features, ratio and distance features. The ANN and SVM were used to develop two independent models using new set of features as input. The models based on CRF and HCRF was used to develop the baseline system for the present study. The experimental results suggest that the proposed new set of features provides improvement in terms of accuracy using ANN (7.48%) and SVM (9.38%) based models as compared with baseline CRF based model. There are also significant improvements in the performances of both ANN (2.08%) and SVM (3.98%) based models as compared with HCRF based model