Application of Deep Learning Techniques on Sign Language Recognition—A Survey

2021 ◽  
pp. 211-227
Author(s):  
Pranjali Barve ◽  
Namita Mutha ◽  
Anagha Kulkarni ◽  
Yashshree Nigudkar ◽  
Yael Robert
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Learning Techniques

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.

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 Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6256
Author(s):  
Boon Giin Lee ◽  
Teak-Wei Chong ◽  
Wan-Young Chung
Keyword(s):  
Deep Learning ◽  
American Sign Language ◽  
Sign Language ◽  
Sensor Fusion ◽  
Recognition Rate ◽  
Recognition System ◽  
Hearing Impaired ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Impaired People

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

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