scholarly journals Smart Finger Gesture Recognition System for Silent Speakers

2019 ◽  
pp. 238-350
Author(s):  
Gayathri. R ◽  
K. Sheela Sobana Rani ◽  
R. Lavanya
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Software Tool ◽  
Recognition System ◽  
Raspberry Pi ◽  
Text Format ◽  
Data Glove ◽  
Signal Conversion ◽  
Audio Output ◽  
Flex Sensors

Silent speakers face a lot of problems when it comes to communicate their thoughts and views. Furthermore, only few people know the sign language of these silent speakers. They tend to feel awkward to take part any exercises with the typical individuals. They require gesture based communication mediators for their interchanges. The solution to this problem is to provide them a better way to take their message across, “Smart Finger Gesture Recognition System for Silent Speakers” which has been proposed. Instead of using sign language, gesture recognition is done with the help of finger movements. The system consists of data glove, flex sensors, raspberry pi. The flex sensors are fitted on the data gloves and it is used to recognize the finger gestures. Then the ADC module is used to convert the analog values into digital form. After signal conversion, the value is given to Raspberry Pi 3, and it converts the signals into audio output as well as text format using software tool. The proposed framework limits correspondence boundary between moronic individuals and ordinary individuals. Therefore, the recognized finger gestures are conveyed into speech and text so that the normal people can easily communicate with dumb people.

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.

scholarly journals Continuous Finger Gesture Recognition Based on Flex Sensors

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3986 ◽  
Author(s):  
Wei-Chieh Chuang ◽  
Wen-Jyi Hwang ◽  
Tsung-Ming Tai ◽  
De-Rong Huang ◽  
Yun-Jie Jhang
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Maximum A Posteriori ◽  
Map Estimation ◽  
A Posteriori ◽  
Sensory Data ◽  
Robust Recognition ◽  
Flex Sensors ◽  
Gated Recurrent Unit ◽  
Gesture Spotting

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.

scholarly journals Sign Language Recognition-A Survey of Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 854-857
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Spoken Language ◽  
Hearing Impaired ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Data Glove ◽  
Depth Sensors ◽  
Impaired People

Sign language is the only method of communication for the hearing and speech impaired people around the world. Most of the speech and hearing-impaired people understand single sign language. Thus, there is an increasing demand for sign language interpreters. For regular people learning sign language is difficult, and for speech and hearing-impaired person, learning spoken language is impossible. There is a lot of research being done in the domain of automatic sign language recognition. Different methods such as, computer vision, data glove, depth sensors can be used to train a computer to interpret sign language. The interpretation is being done from sign to text, text to sign, speech to sign and sign to speech. Different countries use different sign languages, the signers of different sign languages are unable to communicate with each other. Analyzing the characteristic features of gestures provides insights about the sign language, some common features in sign languages gestures will help in designing a sign language recognition system. This type of system will help in reducing the communication gap between sign language users and spoken language users.

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