Abstract
Recognizing sign language is an important interest area since there are many speech and hearing impaired people in the world. They need to be understood by other people and understand them as well. Unfortunately, the number of people who have the knowledge of sign language is not many. In order to communicate with handicapped people, existence of some automatized systems may be helpful. Therefore, in this work, we aimed to implement a system that recognizes the sign language and converts it to text to help people while communicating with each other where the input scene is taken from camera. We produced a training data which includes eight different sign language videos. After that, we used “Motion History Images”(MHI) to extract the motion information from them. A classification is done by using nearest neighbor approach after extracting the features from MHI of videos. As a result, by using training data, our system predicts the text for given sign language. The overall classification accuracy is computed as 95%.
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