Bangla Sign Language Dataset Generation using Depth Information

Rayeed, S. M.; Akram, Gazi Wasif; Zilani, Golam Sadman

dc.contributor.author	Rayeed, S. M.
dc.contributor.author	Akram, Gazi Wasif
dc.contributor.author	Zilani, Golam Sadman
dc.date.accessioned	2022-04-16T16:11:57Z
dc.date.available	2022-04-16T16:11:57Z
dc.date.issued	2021-03-30
dc.identifier.citation	[1] M. S. Islam, S. S. S. Mousumi, N. A. Jessan, A. S. A. Rabby, and S. A. Hossain, “Ishara-lipi: The first complete multipurposeopen access dataset of isolated characters for bangla sign language,” in 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–4, IEEE, 2018. [2] J. K. Chen, D. Sengupta, and R. R. Sundaram, “Cs229 project final report sign language gesture recognition with unsupervised feature learning.” [3] P. Doliotis, A. Stefan, C. McMurrough, D. Eckhard, and V. Athitsos, “Comparing gesture recognition accuracy using color and depth information,” in Proceedings of the 4th international conference on PErvasive technologies related to assistive environments, pp. 1–7, 2011. [4] B. Liao, J. Li, Z. Ju, and G. Ouyang, “Hand gesture recognition with generalized hough transform and dc-cnn using realsense,” in 2018 Eighth International Conference on Information Science and Technology (ICIST), pp. 84–90, IEEE, 2018. [5] P. Molchanov, S. Gupta, K. Kim, and J. Kautz, “Hand gesture recognition with 3d convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 1–7, 2015. [6] N. Pugeault and R. Bowden, “Spelling it out: Real-time asl fingerspelling recognition,” in 2011 IEEE International conference on computer vision workshops (ICCV workshops), pp. 1114–1119, IEEE, 2011. [7] K. O. Rodriguez and G. C. Chavez, “Finger spelling recognition from rgb-d information using kernel descriptor,” in 2013 XXVI Conference on Graphics, Patterns and Images, pp. 1–7, IEEE, 2013. 46 [8] W. Nai, Y. Liu, D. Rempel, and Y. Wang, “Fast hand posture classification using depth features extracted from random line segments,” Pattern Recognition, vol. 65, pp. 1–10, 2017. [9] H. Liang and J. Yuan, “Hand parsing and gesture recognition with a commodity depth camera,” in Computer Vision and Machine Learning with RGB-D Sensors, pp. 239–265, Springer, 2014. [10] L. E. Potter, J. Araullo, and L. Carter, “The leap motion controller: a view on sign language,” in Proceedings of the 25th Australian computer-human interaction conference: augmentation, application, innovation, collaboration, pp. 175–178, 2013. [11] D. Naglot and M. Kulkarni, “Real time sign language recognition using the leap motion controller,” in 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 3, pp. 1–5, 2016. [12] S. Mudduluru, “Indian sign language numbers recognition using intel realsense camera,” 2017. [13] M. Mohandes, S. Aliyu, and M. Deriche, “Arabic sign language recognition using the leap motion controller,” in 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), pp. 960–965, 2014. [14] S. Lang, M. Block, and R. Rojas, “Sign language recognition using kinect,” in International Conference on Artificial Intelligence and Soft Computing, pp. 394–402, Springer, 2012. [15] J. Huang, W. Zhou, H. Li, and W. Li, “Sign language recognition using realsense,” in 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 166–170, IEEE, 2015. [16] J. Mistry and B. Inden, “An approach to sign language translation using the intel realsense camera,” in 2018 10th Computer Science and Electronic Engineering (CEEC), pp. 219–224, IEEE, 2018.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1328
dc.description	Supervised by Md. Kamrul Hasan, PhD, Professor, Co-Supervisor, Mr. Hasan Mahmud, Assistant Professor, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. This task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing dataset for SLR in our country is based on RGB images (converted to grayscale) and CNN is used for classification. However, it is difficult to design model to adapt to the large variations of hand gestures in the dataset, also the computational expense is high. Modern researches on other sign languages have shown that using depth information in Sign Language Recognition (SLR) gives better accuracy, which hasn't been introduced yet in our country. In this paper, we intend to build a complete Bangla Sign Language (BSL) dataset using depth information from depth images. In order to do, we’ll be collecting our depth information from our captured image samples using MediaPipe which is a cross-platform framework for. building multimodal applied machine learning pipeline. It is quite a new and advanced technology in hand tracking and gesture recognition. As opposed to the existing image dataset, we intend to build a feature-based depth dataset, that, if accurately modeled, is more likely to give better results than the existing one in sign language recognition (SLR).	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Bangla Sign Language, MediaPipe, Hand Landmark Model, Intel RealSense, Depth Information, Gesture Recognition, Sign Language Dataset, Hand Key-points, Hand Tracking	en_US
dc.title	Bangla Sign Language Dataset Generation using Depth Information	en_US
dc.type	Thesis	en_US