A novel lightweight CNN approach for Bangladeshi sign language gesture recognition
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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Brac University
2023
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| Acceso en línea: | http://hdl.handle.net/10361/17893 |
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10361-178932023-03-22T06:27:53Z A novel lightweight CNN approach for Bangladeshi sign language gesture recognition Rahman, Aryan Khan, Ahbab Ali Shoumik, Tazwar Mohammed Khondaker, Ms. Arnisha Department of Computer Science and Engineering, Brac University Bangladeshi Sign Language(BDSL) Deep Learning Convolutional Neural Network Image Processing Image Classification Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-35). The impairment of speech impediment affects 6.9% of Bangladesh’s population. This is a condition in which people cannot communicate vocally with others or hear what they are saying, causing them to rely on nonverbal means of commu nication. For such persons, sign language is a common way of communication in which they communicate with others by making various hand gestures and mo tions. The biggest problem is that not everyone understands sign language. Many people cannot converse using sign language, making communication between them problematic. Even though translators and interpreters are available to assist with communication, a more straightforward method is required. We propose a method which uses deep learning combined with some computer vision techniques to detect and classify Bangla sign languages to close this gap. Our custom-made CNN model can recognize and classify Bangla sign language characters from the Ishara-Lipi dataset with a testing accuracy of 99.21%. To recognize the precise indications of a hand gesture and understand what they mean, we trained our model with sufficient samples by augmenting and preprocessing the Ishara-Lipi dataset using various data augmentation techniques. Aryan Rahman Ahbab Ali Khan Tazwar Mohammed Shoumik B. Computer Science 2023-02-14T08:25:50Z 2023-02-14T08:25:50Z 2022 2022-05 Thesis ID: 18201174 ID: 18201190 ID: 18201121 http://hdl.handle.net/10361/17893 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. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Bangladeshi Sign Language(BDSL) Deep Learning Convolutional Neural Network Image Processing Image Classification Machine Learning |
| spellingShingle |
Bangladeshi Sign Language(BDSL) Deep Learning Convolutional Neural Network Image Processing Image Classification Machine Learning Rahman, Aryan Khan, Ahbab Ali Shoumik, Tazwar Mohammed A novel lightweight CNN approach for Bangladeshi sign language gesture recognition |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Khondaker, Ms. Arnisha |
| author_facet |
Khondaker, Ms. Arnisha Rahman, Aryan Khan, Ahbab Ali Shoumik, Tazwar Mohammed |
| format |
Thesis |
| author |
Rahman, Aryan Khan, Ahbab Ali Shoumik, Tazwar Mohammed |
| author_sort |
Rahman, Aryan |
| title |
A novel lightweight CNN approach for Bangladeshi sign language gesture recognition |
| title_short |
A novel lightweight CNN approach for Bangladeshi sign language gesture recognition |
| title_full |
A novel lightweight CNN approach for Bangladeshi sign language gesture recognition |
| title_fullStr |
A novel lightweight CNN approach for Bangladeshi sign language gesture recognition |
| title_full_unstemmed |
A novel lightweight CNN approach for Bangladeshi sign language gesture recognition |
| title_sort |
novel lightweight cnn approach for bangladeshi sign language gesture recognition |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17893 |
| work_keys_str_mv |
AT rahmanaryan anovellightweightcnnapproachforbangladeshisignlanguagegesturerecognition AT khanahbabali anovellightweightcnnapproachforbangladeshisignlanguagegesturerecognition AT shoumiktazwarmohammed anovellightweightcnnapproachforbangladeshisignlanguagegesturerecognition AT rahmanaryan novellightweightcnnapproachforbangladeshisignlanguagegesturerecognition AT khanahbabali novellightweightcnnapproachforbangladeshisignlanguagegesturerecognition AT shoumiktazwarmohammed novellightweightcnnapproachforbangladeshisignlanguagegesturerecognition |
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1814307205125505024 |