Sign language recognition algorithm based on depth image information

2013 ◽  
Vol 33 (10) ◽  
pp. 2882-2885
Author(s):  
Quan YANG ◽  
Jinye PENG
Keyword(s):  
Sign Language ◽  
Recognition Algorithm ◽  
Depth Image ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Image Information

Research on Continuous Sign Language Sentence Recognition Algorithm Based on Weighted Key-Frame

2018 ◽  
Vol 22 (4) ◽  
pp. 483-490
Author(s):  
Xin-Xin Xu ◽  
Yuan-Yuan Huang ◽  
Zuo-Jin Hu ◽  
◽  
Keyword(s):  
Sign Language ◽  
Adaptive Algorithm ◽  
Control Device ◽  
Recognition Algorithm ◽  
Basic Unit ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Key Frame ◽  
Sentence Recognition ◽  
Key Frames

At present, most of the dynamic sign language recognition is only for sign language words, the continuous sign language sentence recognition research and the corresponding results are less, because the segmentation of such sentence is very difficult. In this paper, a sign language sentence recognition algorithm is proposed based on weighted key-frames. Key-frames can be regarded as the basic unit of sign word, therefore, according to key frames we can get related vocabularies, and thus we can further organize these vocabularies into meaningful sentences. Such work can avoid the hard point of dividing sign language sentence directly. With the help of Kinect, i.e. motion-control device, a kind of self-adaptive algorithm of key-frame extraction based on the trajectory of sign language is brought out in the paper. After that, the key-frame is given weight according to its semantic contribution. Finally, the recognition algorithm is designed based on these weighted key-frames and thus get the continuous sign language sentence. Experiments show that the algorithm designed in this paper can realize real-time recognition of continuous sign language sentences.

scholarly journals Application of Kinect Technology in Blind Aerobics Learning

2017 ◽  
Vol 12 (12) ◽  
pp. 103 ◽  
Author(s):  
Hui Qu
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Training System ◽  
Language Recognition ◽  
Joint Angles ◽  
Sign Language Recognition ◽  
Feature Extraction Method ◽  
Joint Coordinates ◽  
Tracking Technology

In order for blind people to learn aerobics more conveniently, we combined Kinect skeletal tracking technology with aerobics-assisted training to design a Kinect-based aerobics-assisted training system. Through the Kinect somatosensory camera, the feature extraction method and recognition algorithm of sign language are improved, and the sign language recognition system is realized. Sign language is translated through the sign language recognition system and expressed in understandable terms, providing a sound way of learning. The experimental results show that the system can automatically collect and recognize the aerobics movements. By comparing with the standard movements in the database, the system evaluates the posture of trainers from the perspectives of joint coordinates and joint angles, followed by the provision of movements contrast graphics and corresponding advice. Therefore, the system can effectively help the blind to learn aerobics.

INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING OPENPOSE

2019 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
MALHOTRA POOJA ◽  
K. MANIAR CHIRAG ◽  
V. SANKPAL NIKHIL ◽  
R. THAKKAR HARDIK ◽  
◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

STATIC DEVNAGARI SIGN LANGUAGE RECOGNITION

2016 ◽  
Vol 3 (3) ◽  
pp. 13
Author(s):  
VERMA VERSHA ◽  
PATIL SANDEEP B. ◽  
◽  
Keyword(s):  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition

Technological Aids for Deaf and Mute in Modern World

2020 ◽  
Vol 14 ◽  
Author(s):  
Vasu Mehra ◽  
Dhiraj Pandey ◽  
Aayush Rastogi ◽  
Aditya Singh ◽  
Harsh Preet Singh
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
Modern World ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Background Elimination ◽  
Sign Recognition ◽  
Technological Advances ◽  
Better Than

Background:: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective:: Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods:: The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. Result:: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion:: It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.

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