%0 Conference Paper %1 10.1007/978-981-99-7093-3_46 %A Mali, Prabhat %A Shakya, Aman %A Panday, Sanjeeb Prasad %B Lecture Notes in Networks and Systems %C Singapore %D 2023 %E Shakya, Subarna %E Tavares, João Manuel R. S. %E Fernández-Caballero, Antonio %E Papakostas, George %I Springer Nature Singapore %K myown %P 697--709 %R 10.1007/978-981-99-7093-3_46 %T Sign Language Recognition Using Long Short-Term Memory Deep Learning Model %U https://doi.org/10.1007/978-981-99-7093-3_46 %V 798 %X This paper aims to present an easy way of communication for speech impaired and hearing impaired people. This paper focuses on the use of neural network techniques, specifically MediaPipe Holistic and the Long Short-Term Memory (LSTM) module, to recognize sign language for individuals with disabilities. The utilization of MediaPipe Holistic, which integrates pose, hand, and face keypoints with precise levels, was used due to its low latency and high tracking accuracy in real-world scenarios. This paper deals with commonly used words of American Standard Sign Language. The result obtained shows that the proposed model can detect American Standard Sign Language with accuracy of 98.50 percentage. %@ 978-981-99-7093-3

@inproceedings{10.1007/978-981-99-7093-3_46, abstract = {This paper aims to present an easy way of communication for speech impaired and hearing impaired people. This paper focuses on the use of neural network techniques, specifically MediaPipe Holistic and the Long Short-Term Memory (LSTM) module, to recognize sign language for individuals with disabilities. The utilization of MediaPipe Holistic, which integrates pose, hand, and face keypoints with precise levels, was used due to its low latency and high tracking accuracy in real-world scenarios. This paper deals with commonly used words of American Standard Sign Language. The result obtained shows that the proposed model can detect American Standard Sign Language with accuracy of 98.50 percentage.}, added-at = {2023-12-16T16:36:31.000+0100}, address = {Singapore}, author = {Mali, Prabhat and Shakya, Aman and Panday, Sanjeeb Prasad}, biburl = {https://www.bibsonomy.org/bibtex/299257e82882898f4d9d0d8997a611f6c/amanshakya}, booktitle = {Lecture Notes in Networks and Systems}, doi = {10.1007/978-981-99-7093-3_46}, editor = {Shakya, Subarna and Tavares, Jo{\~a}o Manuel R. S. and Fern{\'a}ndez-Caballero, Antonio and Papakostas, George}, eventtitle = {Fourth International Conference on Image Processing and Capsule Networks}, interhash = {7161942e5f073f7c66d4fa03baf866a2}, intrahash = {99257e82882898f4d9d0d8997a611f6c}, isbn = {978-981-99-7093-3}, keywords = {myown}, pages = {697--709}, publisher = {Springer Nature Singapore}, series = {Lecture Notes in Networks and Systems}, timestamp = {2023-12-16T16:36:31.000+0100}, title = {Sign Language Recognition Using Long Short-Term Memory Deep Learning Model}, url = {https://doi.org/10.1007/978-981-99-7093-3_46}, volume = 798, year = 2023 }