Linguistic Sign Language Recognition ThoughImage Based Approach

Sign language recognition is a very challenging research area. In this proposed system, a well trained computer system is used to recognize static hand gestures representing linguistic words. The main aim of the paper is conversion of linguistic sign languages into text and speech form. Recognized sign are also translated into Tamil and Hindi languages. It contains three processes of work. First process is pre-processing, in which the obtained images are processed through the steps like segmentation, resize, and gray conversion. Second process is region-based analysis which exploits both boundary and interior pixels of an object. Solidity, perimeter, convex hull, area, major axis length, minor axis length, eccentricity, orientation are some of the shape descriptors used as features in this process. The features derived are used to train the binary classifier first; secondly the testing images are given for classification. Knn classifiers are used for classification which provides a good result with less computation time for larger datasets. Since, the system handled a binary classifier it performed a one-versus-all kind of classification. PCNN (Pulse Coupled Neural Network) is used for pattern recognition. Third process is the hand gesture recognition.