Bangla sign language recognition using leap motion sensor
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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| 语言: | English |
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Brac University
2020
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| 在线阅读: | http://hdl.handle.net/10361/13638 |
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10361-136382022-01-26T10:08:18Z Bangla sign language recognition using leap motion sensor Tan, Tamkin Mahmud Mondol, Anna Mary Nawal, Noshin Ahmed, Sabbir Uddin, Jia Bangla sign language Leap motion controller Machine learning HCI Greedy cloud match Gesture recognition This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-36). Sign language is used by hearing and speech impaired people to transmit their messages to other people but it is difficult for a regular people to understand this gesture based language. Instantaneous responses on sign language can significantly enhance the understanding of sign language. In this paper, we propose a system that detects Bangla Sign Language using a digital motion sensor called Leap Motion Controller. It is a sensor or device which can detect 3D motion of hands, fingers and finger like objects without any contact. A Sign Language Recognition system has to be designed to recognize a hand gesture. In sign language system, gestures are defined as some specific patterns or movement of the hands to give an expression. There has to be a library which includes all the datasets to match with the user given gestures. We have to compare the sequences of data we get from Leap Motion and our datasets to get an optimal result which is basically the output. It will then show the output as text in the display. For our system, we choose to use $P Point-Cloud Recognizer algorithm to match the input data with our datasets. This recognition algorithm was designed for rapid prototyping of gesture-based UI and can deliver an average over 99% accuracy in user-dependent testing. Our proposed model is designed in a way so that the hearing and speech impaired people can communicate easily and efficiently with common people. Tamkin Mahmud Tan Anna Mary Mondol Noshin Nawal Sabbir Ahmed B. Computer Science 2020-01-20T05:23:14Z 2020-01-20T05:23:14Z 2019 2019-08 Thesis ID 15301040 ID 15301056 ID 15301077 ID 15301079 http://hdl.handle.net/10361/13638 en Brac 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. 36 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Bangla sign language Leap motion controller Machine learning HCI Greedy cloud match Gesture recognition |
| spellingShingle |
Bangla sign language Leap motion controller Machine learning HCI Greedy cloud match Gesture recognition Tan, Tamkin Mahmud Mondol, Anna Mary Nawal, Noshin Ahmed, Sabbir Bangla sign language recognition using leap motion sensor |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Uddin, Jia |
| author_facet |
Uddin, Jia Tan, Tamkin Mahmud Mondol, Anna Mary Nawal, Noshin Ahmed, Sabbir |
| format |
Thesis |
| author |
Tan, Tamkin Mahmud Mondol, Anna Mary Nawal, Noshin Ahmed, Sabbir |
| author_sort |
Tan, Tamkin Mahmud |
| title |
Bangla sign language recognition using leap motion sensor |
| title_short |
Bangla sign language recognition using leap motion sensor |
| title_full |
Bangla sign language recognition using leap motion sensor |
| title_fullStr |
Bangla sign language recognition using leap motion sensor |
| title_full_unstemmed |
Bangla sign language recognition using leap motion sensor |
| title_sort |
bangla sign language recognition using leap motion sensor |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/13638 |
| work_keys_str_mv |
AT tantamkinmahmud banglasignlanguagerecognitionusingleapmotionsensor AT mondolannamary banglasignlanguagerecognitionusingleapmotionsensor AT nawalnoshin banglasignlanguagerecognitionusingleapmotionsensor AT ahmedsabbir banglasignlanguagerecognitionusingleapmotionsensor |
| _version_ |
1814307344421486592 |