Portable Hand Gesture Recognition System for Generalized Sign Language

Author(s):  
Jyotsna Singh ◽  
Arpit Jaiswal ◽  
Akshay K Sood ◽  
Anuj Dhillon ◽  
Divyansh Manchanda
2021 ◽  
Author(s):  
Saliya S Shaikh ◽  
Akram A Patel ◽  
Pravadha Deshmukh Pawar ◽  
Rubana P Shaikh

Too many research has been done in the field of Human Computer Interaction (HCI). One of the system called Hand Gesture Recognition (HGR) gives solution to build the HCI systems. Now a days, computer is used as a interpreter between humans. The proposed system is used to recognize the real time static hand gesture of Indian sign language number system zero to nine. In this paper we propose a system for hand gesture recognition which is simple and fast. Based on the proposed algorithm, this system can automatically convert the input hand gesture into the text and audio. The system first capture the image of hand gesture shown by user using a simple webcam then using our proposed algorithm it recognize the gesture. This system can use for real time application due to the use of simple logic condition applied to recognize the gesture. The proposed system is size invariant and implemented using OpenCV.


2021 ◽  
Author(s):  
Mark Benedict D. Jarabese ◽  
Charlie S. Marzan ◽  
Jenelyn Q. Boado ◽  
Rushaine Rica Mae F. Lopez ◽  
Lady Grace B. Ofiana ◽  
...  

Hearing impaired individuals use sign languages to communicate with others within the community. Because of the wide spread use of this language, hard-of-hearing individuals can easily understand it but it is not known by a lot of normal people. In this paper a hand gesture recognition system has been developed to overcome this problem, for those who don't recognize sign language to communicate simply with hard-of-hearing individuals. In this paper a computer vision-based system is designed to detect sign Language. Datasets used in this paper are binary images. These images are given to the convolution neural network (CNN). This model extracts the features of the image and classifies the images, and it recognises the gestures. The gestures used in this paper are of American Sign Language. In real time system the images are converted to binary images using Hue, Saturation, and Value (HSV) colour model. In this model 87.5% of data is used for training and 12.5% of data is used for testing and the accuracy obtained with this model is 97%.


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