10-04-2017, 08:12 PM
deaf and dumb iee
The proposed scheme recognizes and interprets American Sign Language symbols that have static gestures. The system provides an opportunity for deaf and dumb individuals to communicate and learn using computers. American Sign Language is a widely used and accepted standard for communication by people with hearing and speaking impairments. The proposed system recognizes and translates static hand gesture of alphabets in AS L into textual output. This text can further be converted into speech. The user of the system is free from data acquisition devices. The concepts of Principal Component Analysis (PCA) are used on the static gesture images of the AS L alphabet. The PCA features extracted from the image are used to classify the image into one of the AS L alphabet. The recognition of AS L gestures results in a textual output and is then can be converted into speech. Thus, the scheme helps the hearing and speech impaired to talk using computers.The Sign Language is a method of communication for deaf - dumb people. This paper proposes a method that provides a basis for the development of Sign Language Recognition system for one of the south Indian languages. In the proposed method, a set of 32 signs, each representing the binary UP' & DOWN' positions of the five fingers is defined. The images are of the palm side of right hand and are loaded at runtime i.e. dynamic loading. The method has been developed with respect to single user both in training and testing phase. The static images have been pre-processed using feature point extraction method and are trained with 10 numbers of images for each sign. The images are converted into text by identifying the finger tip position of static images using image processing techniques. The proposed method is able to identify the images of the signer which are captured dynamically during testing phase. The results with test images are presented, which show that the proposed Sign Language Recognition System is able to recognize images with 98.125% accuracy when trained with 320 images and tested with 160 images.
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http://ijarcssedocs/papers/Volume_3/9_Se...9-0113.pdf
http://ijcsetdocs/Volumes/volume2issue5/...020506.pdf
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