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dc.contributor.authorAhmad Yahya Dawoden_US
dc.contributor.authorNopasit Chakpitaken_US
dc.description.abstract© 2019 IEEE. Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Fingerspelling recognition method from isolate sign language has attracted research interest in computer vision and human-computer interaction based on a novel technique. The essential for real-time recognition of isolate sign language has grown with the emergence of better-capturing devices such as Kinect sensors. The purpose of this paper is to design a user independent framework for automatic recognition of American Sign Language which can recognize several one-handed dynamic isolated signs and interpreting their meaning. We built datasets as a raw data for alphabets (A-Z) or numbers (1-20) by used left-hand the 3D point (XL, YL, ZL) or switch by right-hand (XR, YR, ZR) centroid as one of contribution. The proposed approach was tested for gestures that involve left-hand or right-hand and was compared with other approach and gave better accuracy. Two machine learning methods are involved like Hidden Conditional Random Field (HCRF), and Random Decision Forest (RDF) for the classification part. The third contribution based on low lighting condition and cluttered background. In this research work is achieved for recognition accuracy over 99.7%.en_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.titleNovel technique for isolated sign language based on fingerspelling recognitionen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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