Thai sign language recognition by using geometric invariant feature and ANN classification

Hand sign language is the primary communication tool for people with hearing-impaired or deaf. People can use it to communicate effectively but the challenge is to communicate with the computer. Human computer interaction (HCI) will have a positive impact on their use. Thus, this is to bring the hand gestures in HCI as an important research area. This research focuses on 2D image recognition utilizing an evolved geometric invariant feature and also have developed a two-layer feedforward neural network to identify and translate hand gesture pose of the 42 letters in the Thai Sign Language (TSL) alphabet to Thai alphabets. We designed glove with six different colored markers for using in the experiment. The result shows that this system is able to recognize 42 TSL alphabets with an average accuracy of 96.19 %.