Performance enhancement by combining visual clues to identify sign language motions

Abstract

This paper presents a sign language recognition method that uses gloves with colored regions and an optical camera. Hand and finger motions can be identified by the movement of the colored regions. The authors propose using six weak cues from each sign language motion, as determined by an HMM (Hidden Markov Model). Decoding and recognition is achieved by detecting characteristic combinations of cues. It was experimentally verified that an accurate recognition rate as high as 62.3% was achieved by looking for six cues per word while observing a list of 25 sign language words.