Off-Chip Training with Additive Perturbation for FPGA-Based Hand Sign Recognition System

2018 ◽  
Vol E101.A (2) ◽  
pp. 499-506
Author(s):  
Hiroomi HIKAWA ◽  
Masayuki TAMAKI ◽  
Hidetaka ITO
Keyword(s):  
Recognition System ◽  
Sign Recognition ◽  
Additive Perturbation

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.

A Real-Time Speed Limit Sign Recognition System for Autonomous Vehicle Using SSD Algorithm

2021 ◽  
Author(s):  
Nur Nabilah Abu Mangshor ◽  
Nor Syahirah Saharuddin ◽  
Shafaf Ibrahim ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
Khyrina Airin Fariza Abu Samah
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Recognition System ◽  
Speed Limit ◽  
Sign Recognition

Assistive Technology based Solution for Hearing Impairment using Smartphone

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Gesture Recognition ◽  
Video Sequence ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Background Elimination ◽  
Sign Recognition ◽  
Limited Support ◽  
Recognition Capability ◽  
Better Than

Developing a system for sign language recognition becomes essential for the 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 the exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. 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 a human-computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models that have been trained by using TensorFlow and Keras library. The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV

Sign Recognition System for Differently Abled People

2018 ◽  
Author(s):  
Sidra Syed ◽  
Seemeen Chagani ◽  
Mehak Hafeez ◽  
Sybil Timothy ◽  
Hira Zahid
Keyword(s):  
Recognition System ◽  
Sign Recognition
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