%0 Journal Article %1 noauthororeditor %A Alice, D. Anbarasan R. Aravind K. %D 2018 %J International Journal of Trend in Scientific Research and Development %K (CNN) (HMM) (SVM) Computer Convolutional Engineering Gesture Hidden Image Machine Markov Model Network Neural Support Vector classification extraction pattern recognition %N 2 %P 1221-1225 %T GRS '“ Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People %U http://www.ijtsrd.com/engineering/computer-engineering/9638/grs-'“-gesture-based-recognition-system-for-indian-sign-language-recognition-system-for-deaf-and-dumb-people/d-anbarasan %V 2 %X Recognition languages are developed for the better communication of the challenged people. The recognition signs include the combination of various with hand gestures, movement, arms and facial expressions to convey the words thought. The languages used in sign are rich and complex as equal as to languages that are spoken. As the technological world is growing rapidly, the sign languages for human are made to recognised by systems in order to improve the accuracy and the multiply the various sign languages with newer forms. In order to improve the accuracy in detecting the input sign, a model has been proposed. The proposed model consists of three phases a training phase, a testing phase and a storage output phase. A gesture is extracted from the given input picture. The extracted image is processed to remove the background noise data with the help of threshold pixel image value. After the removal of noise from the image and the filtered image to trained model is tested with a user input and then the detection accuracy is measured. A total of 50 sign gestures were loaded into the training model. The trained model accuracy is measured and then the output is extracted in the form of the mentioned language symbol. The detection mechanism of the proposed model is compared with the other detection methods such as Hidden Markov Model(HMM), Convolutional Neural Networks(CNN) and Support Vector Machine(SVM). The classification is done by means of a Support Vector Machine(SVM) which classifies at a higher accuracy. The accuracy obtained was 99 percent in comparison with the other detection methods. D. Anbarasan | R. Aravind | K. Alice"GRS '“ Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9638.pdf http://www.ijtsrd.com/engineering/computer-engineering/9638/grs-'“-gesture-based-recognition-system-for-indian-sign-language-recognition-system-for-deaf-and-dumb-people/d-anbarasan

@article{noauthororeditor, abstract = {Recognition languages are developed for the better communication of the challenged people. The recognition signs include the combination of various with hand gestures, movement, arms and facial expressions to convey the words thought. The languages used in sign are rich and complex as equal as to languages that are spoken. As the technological world is growing rapidly, the sign languages for human are made to recognised by systems in order to improve the accuracy and the multiply the various sign languages with newer forms. In order to improve the accuracy in detecting the input sign, a model has been proposed. The proposed model consists of three phases a training phase, a testing phase and a storage output phase. A gesture is extracted from the given input picture. The extracted image is processed to remove the background noise data with the help of threshold pixel image value. After the removal of noise from the image and the filtered image to trained model is tested with a user input and then the detection accuracy is measured. A total of 50 sign gestures were loaded into the training model. The trained model accuracy is measured and then the output is extracted in the form of the mentioned language symbol. The detection mechanism of the proposed model is compared with the other detection methods such as Hidden Markov Model(HMM), Convolutional Neural Networks(CNN) and Support Vector Machine(SVM). The classification is done by means of a Support Vector Machine(SVM) which classifies at a higher accuracy. The accuracy obtained was 99 percent in comparison with the other detection methods. D. Anbarasan | R. Aravind | K. Alice"GRS '“ Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9638.pdf http://www.ijtsrd.com/engineering/computer-engineering/9638/grs-'“-gesture-based-recognition-system-for-indian-sign-language-recognition-system-for-deaf-and-dumb-people/d-anbarasan }, added-at = {2018-09-04T06:57:14.000+0200}, author = {Alice, D. Anbarasan R. Aravind K.}, biburl = {https://www.bibsonomy.org/bibtex/27e527cdd96839c59ec0e5480b29669db/ijtsrd}, interhash = {8dd8e9f9f15ff3869095ae2602c41c60}, intrahash = {7e527cdd96839c59ec0e5480b29669db}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {(CNN) (HMM) (SVM) Computer Convolutional Engineering Gesture Hidden Image Machine Markov Model Network Neural Support Vector classification extraction pattern recognition}, language = {English}, month = feb, number = 2, pages = {1221-1225}, timestamp = {2018-10-02T11:01:27.000+0200}, title = {GRS '“ Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People }, url = {http://www.ijtsrd.com/engineering/computer-engineering/9638/grs-'“-gesture-based-recognition-system-for-indian-sign-language-recognition-system-for-deaf-and-dumb-people/d-anbarasan}, volume = 2, year = 2018 }