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Here, we have used height of the hand trajectory as a salient feature for separating out the meaningful signs from the movement epenthesis patterns. Further, we have incorporated a unique set of spatial and temporal features for efficient recognition of the signs encapsulated within the continuous sequence. The implementation of an efficient hand segmentation and hand tracking technique makes our system robust to complex background as well as background with multiple signers. Experiments have established that our proposed system can identify signs from a continuous sign stream with a 92.8% spotting rate.<\/jats:p>","DOI":"10.1515\/jisys-2016-0009","type":"journal-article","created":{"date-parts":[[2016,6,19]],"date-time":"2016-06-19T06:01:35Z","timestamp":1466316095000},"page":"471-481","source":"Crossref","is-referenced-by-count":18,"title":["Movement Epenthesis Detection for Continuous Sign Language Recognition"],"prefix":"10.1515","volume":"26","author":[{"given":"Ananya","family":"Choudhury","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering , Gauhati University , Guwahati , Assam, India"}]},{"given":"Anjan","family":"Kumar Talukdar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering , Gauhati University , Guwahati , Assam, India"}]},{"given":"Manas","family":"Kamal Bhuyan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering , Indian Institute of Technology , Guwahati , Assam, India"}]},{"given":"Kandarpa","family":"Kumar Sarma","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Technology , Gauhati University , Guwahati , Assam, India , e-mail:"}]}],"member":"374","published-online":{"date-parts":[[2016,6,20]]},"reference":[{"key":"2025120523272183702_j_jisys-2016-0009_ref_001_w2aab3b7d268b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"M. 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