Depth Based Bangla Sign Language Data-set Generation

Afan, Ifaz Ahmed; Mahmud, Junaid

dc.contributor.author	Afan, Ifaz Ahmed
dc.contributor.author	Mahmud, Junaid
dc.date.accessioned	2020-10-28T09:52:05Z
dc.date.available	2020-10-28T09:52:05Z
dc.date.issued	2019-11-15
dc.identifier.citation	[1] Brashear, Helene and Henderson, Valerie and Park, Kwang- Hyun and Hamilton, Harley and Lee, Seungyon and Starner, ThadAmerican Sign Language Recognition in Game Development for Deaf Children , 2006 [2] M. M. Sole and M. S. Tsoeu Sign language recognition using the Extreme Learning Machine , 2011 [3] Koller, O, Zargaran, O, Ney, H, Bowden, R Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition, 2016 [4] Ali Karami,Bahman Zanj,Azadeh Kiani Sarkaleh Persian sign language (PSL) recognition using wavelet transform and neural networks, 2011 [5] Tomas Simon, Hanbyul Joo, Iain Matthews, Yaser Sheikh Hand keypoint detection in single images using multiview bootstrapping, 2017 [6] S. K. Kang and M. Y. Nam and P. K. Rhee Color Based Hand and Finger Detection Technology for User Interaction, 2008 key7 Yue Wang, Dinggang Shen, Eam Khwang Teoh Lane detection using spline model, 2000 [7] J. Suarez and R. R. Murphy Hand gesture recognition with depth images: A review, 2012 36 [8] Brashear, Helene and Henderson, Valerie and Park, Kwang- Hyun and Hamilton, Harley and Lee, Seungyon and Starner, ThadAmerican Sign Language Recognition in Game Development for Deaf Children , 2006 [9] M. M. Sole and M. S. Tsoeu Sign language recognition using the Extreme Learning Machine , 2011 [10] Koller, O, Zargaran, O, Ney, H, Bowden, R Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition, 2016 [11] Ali Karami,Bahman Zanj,Azadeh Kiani Sarkaleh Persian sign language (PSL) recognition using wavelet transform and neural networks, 2011 [12] Tomas Simon, Hanbyul Joo, Iain Matthews, Yaser Sheikh Hand keypoint detection in single images using multiview bootstrapping, 2017 [13] S. K. Kang and M. Y. Nam and P. K. Rhee Color Based Hand and Finger Detection Technology for User Interaction, 2008 key15 Yue Wang, Dinggang Shen, Eam Khwang Teoh Lane detection using spline model, 2000 [14] J. Suarez and R. R. Murphy Hand gesture recognition with depth images: A review, 2012 [15] Fabio Dominio,Mauro Donadeo,Pietro Zanuttigh Combining multiple depth-based descriptors for hand gesture recognition, 2014 37 [16] M. M. Sole and M. S. Tsoeu Sign language recognition using the Extreme Learning Machine, 2011 [17] Z. Ren and J. Meng and J. Yuan Depth camera based hand gesture recognition and its applications in Human-Computer- Interaction, 2011 [18] C. Wang and Z. Liu and S. Chan Superpixel-Based Hand Ges- ture Recognition With Kinect Depth Camera, 2015 38	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/617
dc.description	Supervised by Prof. Dr. Md. Kamrul Hasan	en_US
dc.description.abstract	Hearing impaired people have own language called Sign Language but it is di cult for understanding to general people. Sign language is the basic method of communication for deaf people during their everyday of life. Sign digits are also a major part of sign language. So machine translator is necessary to allow them to communicate with general people. For making their language understandable to general people, computer vision based solutions are well known nowadays. In this research work we aims at constructing a model in deep learn- ing approach to generate Bangla Sign Language (BdSL) digits. In this approach there use Depth Based Images to train particular signs with a respective training dataset for acquiring our aim. This model will contribute for moving one step forward to make BdSL machine translator. And open up eld for future works.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh	en_US
dc.subject	Depth Image, Sign Language, Intel Real Sense, Dataset, Convolution Neural Network (CNN), Hand Gestures.	en_US
dc.title	Depth Based Bangla Sign Language Data-set Generation	en_US
dc.type	Thesis	en_US