An implementation of the Korean sign language recognizer using neural network based on the post PC

A traditional studies about recognition and representation technology of sign language have several restrictions such as conditionality in space and limitation of motion according to the technology of wire communication, problem of image capture system or video processing system for an acquisition of sign language signals, and the sign language recognition system based on word and morpheme. In order to overcome these restrictions and problems, in this paper, we implement the Korean sign language recognizer in the shape of sentence using neural network based on the Post wearable PC platform. The advantages of our approach are as follows: 1) it improves efficiency of the sign language input module according to the technology of wireless communication, 2) it recognizes and represents continuous sign language of users with flexibility in real time, and 3) it is possible more effective and free interchange of ideas and information between deaf person and hearing person (the public). Experimental result shows the average recognition rate of 92.8% about significant, dynamic and continuous the Korean sign language.