Ethiopian sign language recognition using Artificial Neural Network

Pattern recognition is very challenging multidisciplinary research area attracting researchers and practitioners. Gesture recognition is a specialized pattern recognition task with the goal of interpreting human gestures via mathematical models. One of the usages of gesture recognition is the sign language recognition which is the basic communication method between deaf people. Since there is lack of proficient sign language teachers at schools for the deaf, the teaching and learning process is remaining affected. A system is therefore required to overcome communication barriers facing the deaf community. So, in this paper, a hand gesture detection and recognition system for Ethiopian Sign Language (ESL) has been proposed. Gabor Filter (GF) together with Principal Component Analysis (PCA) has been used for extracting features from the digital images of hand gestures while Artificial Neural Network (ANN) is used for recognizing the ESL from extracted features and to translate into Amharic voice. The experimental results show that the system has produced recognition rate of 98.53%.

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