A REVIEW ON THE DEVELOPMENT OF INDONESIAN SIGN LANGUAGE RECOGNITION SYSTEM

Abstract

Sign language is mainly employed by hearing-impaired people to communicate with each other. However, communication with normal people is a major handicap for them since normal people do not understand their sign language. Sign language recognition is needed for realizing a human oriented interactive system that can perform an interaction like normal communication. Sign language recognition basically uses two approaches: (1) computer vision-based gesture recognition, in which a camera is used as input and videos are captured in the form of video files stored before being processed using image processing; (2) approach based on sensor data, which is done by using a series of sensors that are integrated with gloves to get the motion features finger grooves and hand movements. Different of sign languages exist around the world, each with its own vocabulary and gestures. Some examples are American Sign Language (ASL), Chinese Sign Language (CSL), British Sign Language (BSL), Indonesian Sign Language (ISL) and so on. The structure of Indonesian Sign Language (ISL) is different from the sign language of other countries, in that words can be formed from the prefix and or suffix. In order to improve recognition accuracy, researchers use methods, such as the hidden Markov model, artificial neural networks and dynamic time warping. Effective algorithms for segmentation, matching the classification and pattern recognition have evolved. The main objective of this study is to review the sign language recognition methods in order to choose the best method for developing the Indonesian sign language recognition system.