Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/484965
Title: Computer Vision based Approach for Indian Sign Language Recognition and Translation using Deep Learning
Researcher: Mistree, Kinjal Bhargavkumar
Guide(s): Thakor, Devendra V and Bhatt, Brijesh S
Keywords: Computer Engineering
Engineering and Technology
Machine Learning
University: Uka Tarsadia University
Completed Date: 2023
Abstract: Sign language, a language used by the Deaf, is an entirely visual language with its own grammar and differs mainly from spoken language. Translation between two spoken languages is much smoother than translation between a spoken language and a sign language due to several factors. Deaf people find it very difficult to express their feelings to the rest of society since other people lack the knowledge of the sign language used by the Deaf community. The conception of the system that would be able to translate the sign language into text would facilitate the understanding of the sign language to the rest of society. Such a system would eliminate the dependency on the translators to understand the sign language. newlineThere is significant variation between sign languages of different countries worldwide, although there are many similarities. Therefore, it may not be preferred for Indian Sign Language to fully adopt such approaches for sign language recognition (ISL). After nearly 30 years of research, ISL recognition is still in its infancy when compared to other international sign languages. Many device-based and vision-based approaches have been used for ISL recognition from images of signs. Still, most of these approaches focus on regional versions or manual components of signs having one or two words. It is challenging to handle continuous sign language sentence recognition and translation into the text as no large dataset and corpora are available for videos of ISL sentences. newlineIn our first proposed approach, we have attempted to answer one of the research questions: how to use deep learning to deal with very small amounts of input videos to incorporate both left-handed and right-handed signs without affecting ISL sentence recognition performance. Deep learning gives promising results in gesture recognition, but it requires large datasets to avoid overfitting. To avoid this problem, the concept of image augmentation and pretrained model is used to increase dataset size and reduce over fitting.
Pagination: xxv;140p
URI: http://hdl.handle.net/10603/484965
Appears in Departments:Faculty of Engineering and Technology

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02_certificates.pdf6.32 MBAdobe PDFView/Open
03_contents.pdf55.37 kBAdobe PDFView/Open
04_abstract.pdf77.41 kBAdobe PDFView/Open
05__chapter 1.pdf206.68 kBAdobe PDFView/Open
06_chapter 2.pdf347.13 kBAdobe PDFView/Open
07_chapter 3.pdf93.58 kBAdobe PDFView/Open
08_chapter 4.pdf1.75 MBAdobe PDFView/Open
09_chapter 5.pdf125.08 kBAdobe PDFView/Open
10_chapter 6.pdf163.5 kBAdobe PDFView/Open
11_chapter 7.pdf807.98 kBAdobe PDFView/Open
12_chapter 8.pdf333.97 kBAdobe PDFView/Open
13_chapter 9.pdf1.61 MBAdobe PDFView/Open
14_chapter 10.pdf97.54 kBAdobe PDFView/Open
15_chapter 11.pdf52.08 kBAdobe PDFView/Open
16_chapter 12.pdf49.36 kBAdobe PDFView/Open
17_appendix.pdf3.51 MBAdobe PDFView/Open
80_recommendation.pdf172.41 kBAdobe PDFView/Open
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