-
Anbarasan D
- Student At G.K.M. College Of Engineering And Technology
50 Views
0 Comments
Recognition languages are developed for the better communication of the challenged people. The recognition signs include the combination of various signs/symbols 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 recognise by systems in order to improve accuracy and 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 with the help of threshold pixel image value. Then the filtered images are stored in the database. The trained model is tested with a user input and then the detection accuracy is measured. A total of 260 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), Convolution Neural Networks (CNN) and Support Vector Machine (SVM). The classification is done by means of a Probabilistic Neural Network (PNN) which classifies at a higher accuracy. The accuracy obtained was 99 percent in comparison with the other detection methods.
-
Other Relevant Documents
-
Other Relevant Images
-
-
- Social Share
for this Blog
- Social Share
-
Comment
Porfolio And Blogs
Anbarasan D follows various companies, these companies and employers can view Anbarasan's resume.
