scholarly journals A Review on Deep Learning Based Lip-Reading

2020 ◽  
pp. 182-188
Author(s):  
Kartik Datar ◽  
Meet N. Gandhi ◽  
Priyanshu Aggarwal ◽  
Mayank Sohani
Keyword(s):  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Virtual Reality System ◽  
Sign Language Recognition ◽  
Huge Data ◽  
Movement Recognition ◽  
Lip Reading ◽  
The World ◽  
End To End

In the world of development and advancement, deep learning has made its significant impact in certain tasks in such a way which seemed impossible a few years ago. Deep learning has been able to solve problems which are even complex for machine learning algorithms. The task of lip reading and converting the lip moments to text is been performed by various methods, one of the most successful methods for the following is Lip-net they provide end to end conversion form lip to text. The end to end conversion of lip moments to the words is possible because of availability of huge data and development of different deep learning methods such as Convolution Neural Network and Recurrent Neural Networks. The use of Deep Learning in lip reading is a recent concept and solves upcoming challenges in real-world such as Virtual Reality system, assisted driving systems, sign language recognition, movement recognition, improving hearing aid via Google lens. Various other approaches along with different datasets are explained in the paper.

scholarly journals Bangla Sign Language Recognition and Sentence Building Using Deep Learning

2020 ◽  
Author(s):  
Safayet Anowar Shurid ◽  
Khandaker Habibul Amin ◽  
Md. Shahnawaz Mirbahar ◽  
Dolan Karmaker ◽  
Mohammad Tanvir Mahtab ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition

KArSL

2021 ◽  
Vol 20 (1) ◽  
pp. 1-19
Author(s):  
Ala Addin I. Sidig ◽  
Hamzah Luqman ◽  
Sabri Mahmoud ◽  
Mohamed Mohandes
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Markov Models ◽  
Body Language ◽  
Arab Countries ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

Hear Sign Language: A Real-time End-to-End Sign Language Recognition System

2020 ◽  
pp. 1-1
Author(s):  
Zhibo Wang ◽  
Tengda Zhao ◽  
Jinxin Ma ◽  
Hongkai Chen ◽  
Kaixin Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
End To End

Sign Language Recognition Using Deep Learning and Computer Vision

2020 ◽  
Vol 12 (05-SPECIAL ISSUE) ◽  
pp. 964-968
Author(s):  
Sabeenian R.S ◽  
S. Sai Bharathwaj ◽  
M. Mohamed Aadhil
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition

scholarly journals Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

2021 ◽  
Vol 251 ◽  
pp. 03057
Author(s):  
Michael Andrews ◽  
Bjorn Burkle ◽  
Shravan Chaudhari ◽  
Davide Di Croce ◽  
Sergei Gleyzer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Top Quark ◽  
Learning Algorithms ◽  
Open Data ◽  
High Energy ◽  
Machine Learning Algorithms ◽  
Multiple Gpus ◽  
Event Identification ◽  
End To End

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.

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