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dc.contributor.advisorRhaman, Md. Khalilur
dc.contributor.authorIslam, Ishraqul
dc.contributor.authorIslam, Md. Saqif
dc.contributor.authorProvat, Mahin Islam
dc.contributor.authorKhandakar, Shaneen Shadman
dc.contributor.authorKarim, Fardin Junayed
dc.date.accessioned2023-10-16T04:17:33Z
dc.date.available2023-10-16T04:17:33Z
dc.date.copyright©2022
dc.date.issued2022-09-28
dc.identifier.otherID 19141008
dc.identifier.otherID 19101238
dc.identifier.otherID 19101074
dc.identifier.otherID 19101176
dc.identifier.otherID 19101198
dc.identifier.urihttp://hdl.handle.net/10361/21831
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 36-38).
dc.description.abstractYears of technological progress have made machines capable of identifying humans in images and videos. Moreover, machines like computers can also detect our hand gestures. Gesture recognition is the tool needed to comprehend sign languages. Sign language recognition is an important part of computer vision that uses the visual-manual modality of expression. This method solves the communication barrier between the deaf and mute and the common people. Currently, in the world, there are around 432 Million deaf mutes which is around 5% of the total global population. To solve this problem of communication gap we are focusing on creating an application for detecting sign language which will detect hand gestures and show us the output in the form of text. There are different sign languages present, but in our paper, we are mainly dealing with American Sign Language ( ASL ). Thus for this research, there are certain datasets present on the internet but we will be collecting our own set of words via our Real-time data collection system and make the sentences by using our model. To develop this model we are using both Long Short Term Memory. LSTM networks are a class of RNN that may learn order dependency in sequence prediction challenges. This is a necessary characteristic in complicated problem fields such as machine translation, and speech recognition, therefore we will be using it to recognize the gesture from images and video captured via the camera or webcam. Furthermore, to detect the pose and model it, we are using the MediaPipe Holistic library with the help of OpenCV. This helps us draw the landmarks on skeleton poses. Thus, giving us a generalized overview of an individual’s appearance and background, allowing more focus on the perception of motion. Hence, extracting features from each frame of our videos and then composing them onto LSTM lead us into naming our model Frame Composition LSTM.en_US
dc.description.statementofresponsibilityIshraqul Islam
dc.description.statementofresponsibilityMd. Saqif Islam
dc.description.statementofresponsibilityMahin Islam Provat
dc.description.statementofresponsibilityShaneen Shadman Khandakar
dc.description.statementofresponsibilityFardin Junayed Karim
dc.format.extent48 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses 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.
dc.subjectFrame composition LSTMen_US
dc.subjectASLen_US
dc.subjectMediaPipe holisticen_US
dc.subjectHand gesture recognitionen_US
dc.subject.lcshHuman-computer interaction
dc.subject.lcshComputer communication systems
dc.titleMotion based gesture detection using frame composition LSTMen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science


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