Effect of thinning extent on ASL number recognition using open-finger distance feature measurement technique

In recent years, much of the research is done in using computers to recognize sign language. Computer recognition of sign language is an important research problem for enabling communication with hearing impaired people without the help of interpreter. In this article we propose a method to detect the static image based number of American Sign Language (ASL). This method is based on counting the open fingers in the static images and extracting the feature vector based on the successive distance between the adjacent open fingers. Further neural network is used for the classification of these numbers. This method is qualified to provide an average recognition rate of 92 percent.