Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Main Authors: | , , , , |
|---|---|
| 其他作者: | |
| 格式: | Thesis |
| 语言: | English |
| 出版: |
Brac University
2021
|
| 主题: | |
| 在线阅读: | http://hdl.handle.net/10361/15758 |
| id |
10361-15758 |
|---|---|
| record_format |
dspace |
| spelling |
10361-157582022-01-26T10:13:16Z Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN Dewanjee, Tanmoy Nuder, Azibun Malek, Md. Imtiaz Nanjiba, Refah Rahman, Atia Anjum Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University BdSL Video Processing Machine Learning Sign Language Classification OpenPose Deep Learning LSTM based RNN Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 56-59). Sign-language recognition is an essential part of computer vision to solve a communication obstacle between the deaf-mute and the common. Bangladeshi Sign Language (BdSL) is the medium of communication of the deaf and dumb community of Bangladesh. Where 2.4 million people cannot communicate without a sign language, developing countries like Bangladesh do not have sufficient facilities for these people [34]. Our research represents a sign-language recognizer in Bangladesh which is an approach to understanding Bangladeshi sign language so that it can become a bridge between the deaf-mute community and the normal world. Though many works have been done in this field for foreign languages, there are only a few remarkable works on the Bangladeshi Sign Language, among which they used techniques that are not accessible to all, and their accuracy was also not satisfactory. Moreover, there is a shortage of publicly available datasets of Bangladeshi Sign Language. Our objective is to deliver a compact and highly accurate system that will recognize Bangladeshi Sign Language. We propose an method based on estimation of the human keypoints. First of all, we develop a BdSL dataset containing 1151 videos with ten different words. Our algorithm uses OpenPose to extract human pose from 2D videos and feed the extracted features keeping their temporal nature to an LSTM based RNN classifier that accurately classifies the signs. Our proposed sign language model classifies the signs of Bangladeshi Sign Language with 96.54% accuracy. Tanmoy Dewanjee Azibun Nuder Md. Imtiaz Malek Refah Nanjiba Atia Anjum Rahman B. Computer Science 2021-12-26T05:25:55Z 2021-12-26T05:25:55Z 2021 2021-02 Thesis ID 16301150 ID 16301045 ID 16101068 ID 16301153 ID 16301002 http://hdl.handle.net/10361/15758 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. 59 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
BdSL Video Processing Machine Learning Sign Language Classification OpenPose Deep Learning LSTM based RNN Machine Learning |
| spellingShingle |
BdSL Video Processing Machine Learning Sign Language Classification OpenPose Deep Learning LSTM based RNN Machine Learning Dewanjee, Tanmoy Nuder, Azibun Malek, Md. Imtiaz Nanjiba, Refah Rahman, Atia Anjum Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Dewanjee, Tanmoy Nuder, Azibun Malek, Md. Imtiaz Nanjiba, Refah Rahman, Atia Anjum |
| format |
Thesis |
| author |
Dewanjee, Tanmoy Nuder, Azibun Malek, Md. Imtiaz Nanjiba, Refah Rahman, Atia Anjum |
| author_sort |
Dewanjee, Tanmoy |
| title |
Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN |
| title_short |
Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN |
| title_full |
Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN |
| title_fullStr |
Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN |
| title_full_unstemmed |
Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN |
| title_sort |
recognition of bangladeshi sign language from 2d videos using openpose and lstm based rnn |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15758 |
| work_keys_str_mv |
AT dewanjeetanmoy recognitionofbangladeshisignlanguagefrom2dvideosusingopenposeandlstmbasedrnn AT nuderazibun recognitionofbangladeshisignlanguagefrom2dvideosusingopenposeandlstmbasedrnn AT malekmdimtiaz recognitionofbangladeshisignlanguagefrom2dvideosusingopenposeandlstmbasedrnn AT nanjibarefah recognitionofbangladeshisignlanguagefrom2dvideosusingopenposeandlstmbasedrnn AT rahmanatiaanjum recognitionofbangladeshisignlanguagefrom2dvideosusingopenposeandlstmbasedrnn |
| _version_ |
1814308071318487040 |