Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Image Retrieval of Songket Motifs Using Simple Shape Descriptors
GMAI '06 Proceedings of the conference on Geometric Modeling and Imaging: New Trends
SignTutor: An Interactive System for Sign Language Tutoring
IEEE MultiMedia
Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic fruit and vegetable classification from images
Computers and Electronics in Agriculture
Human-inspired search for redundancy in automatic sign language recognition
ACM Transactions on Applied Perception (TAP)
Appearance-Based recognition of words in american sign language
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
Segmentation of the face and hands in sign language video sequences using color and motion cues
IEEE Transactions on Circuits and Systems for Video Technology
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Sign language is a formal language used by the deaf and dumb people to communicate through bodily movement, especially of hands rather than speech. In this paper, we have presented a vision-based method for recognition of isolated sign considering static and dynamic behaviour of Indian sign language ISL. The proposed methodology consists of three modules: preprocessing, feature extraction and classification. In the preprocessing module, various steps such as skin colour segmentation, redundant frames removal RFR algorithm and face elimination have been performed. The purpose of RFR algorithm is to remove redundant frames from the sign video to speed up the recognition task. In the feature extraction module, multiple features have been extracted. A multi-class support vector machine MSVM and Bayesian K-nearest neighbour BKNN are used to classify the signs. Experimentation with vocabulary of 21 sign from ISL is conducted and the results prove that the proposed method for recognition of gestured sign is effective and having high accuracy. Experimental results demonstrate that the proposed system can recognise signs with 95.3% accuracy.