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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.<\/jats:p>","DOI":"10.4018\/ijmcmc.2017070105","type":"journal-article","created":{"date-parts":[[2017,8,18]],"date-time":"2017-08-18T12:25:09Z","timestamp":1503059109000},"page":"66-78","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Shape and Texture Features for Finger Spelling Recognition in Big Data Applications"],"prefix":"10.4018","volume":"8","author":[{"given":"Yong","family":"Hu","sequence":"first","affiliation":[{"name":"Jinling Institute of Technology, Nanjing, China"}]}],"member":"2432","reference":[{"key":"IJMCMC.2017070105-0","unstructured":"Chang, C. 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