Alam, Dr. Md. AshrafulAhmed, Khan RafinMumu, Sabrin MomotazShuvra, Fatema Tuz Zohora2018-02-182018-02-1820172017ID 12201111ID 13101115ID 13101114http://hdl.handle.net/10361/9490Cataloged from PDF version of thesis report.Includes bibliographical references (pages 33-34).This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.How do a deaf of mute people communicate with each other? Or, even bigger question, how do they make other people understand their messages? The answer is, Sign Language. It is not that easy, most people do not know sign languages. To improve that situation, there have been many researches going on to translate sign language into spoken language. The main purpose of our thesis was to build a system similar to that in Bangla language. When one signs a language, he mostly uses his hands and head. The facial impression is also important. In our system, we tracked the skeleton with the help of Kinect. The system performs hand segmentation, finger identification and the number of fingers the signer has used using K-curvature algorithm while making the gesture. To determine the movement, the information about the skeleton joints is checked from the data set for each gesture. Finally the system shows the output for which the conditions are matched. Thus, the system recognizes the sign language gestures. Keywords: Bangla Sign Language, Kinect, Skeleton Tracking, Finger Identification, Kcurvature algorithm.34 pagesenBRAC University thesis reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Sign languageMicrosoft kinectSpoken languageSkeleton trackingFinger identificationKcurvature algorithmThesis