Evaluation of features for automated transcription of dual-handed sign language alphabets
Himanshu Lilha, Devashish Shivmurthy · 2011
Sign language helps the deaf and mute to communicate effectively. The paper demonstrates the evaluation of various feature extraction techniques for the dual -handed sign language alphabets. The efficiency of features like Histogram of Orientation Gradient (HOG) is discussed followed by the demonstration of the Histogram of Edge Frequency (HOEF) which overcomes the short coming of HOG. The evaluation of HOG accuracy is found to be 71.4% whereas with HOEF it is found to be 98.1%. The paper also demonstrate the overall system for the sign language recognition.