A Comparative Study of Shape and Texture Features for Finger Spelling Recognition in Big Data Applications

With a wide variety of big data applications, Sign Language Recognition has become one of the most important research areas in the field of human-computer interaction. Despite recent progresses, the task of classifying finger spelling is still very challenging in Sign Language Recognition. The visually similarity of some signs, the invisibility of the thumb and the large amount of variation by different signers are all make the hand shape recognition very challenging. The work presented in this paper aims to evaluate the performance of some state-of-the-art features for static finger spelling of alphabets in sign language recognition. The comparison experiments were implemented and tested using two popular data sets. Based on the experimental results, analysis and recommendations are given on the efficiency and capabilities of the compared features.