Artificial neural network based method for Indian sign language recognition

2013 ◽  
Author(s):  
V. Adithya ◽  
P. R. Vinod ◽  
Usha Gopalakrishnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Artificial Neural ◽  
Indian Sign Language

scholarly journals Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

2021 ◽  
Vol 9 (1) ◽  
pp. 182-203
Author(s):  
Muthu Mariappan H ◽  
Dr Gomathi V
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Real Time ◽  
Sign Language ◽  
Gesture Recognition ◽  
Language Translation ◽  
Video Gaming ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several computer vision solutions and the latter by the Recurrent Neural Network (RNN), which is more efficient in handling the sequences of movements. Thus, the real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture. The system is trained with the proposed CasTalk-ISL dataset. The ultimate purpose of the presented research is to deploy a real-time sign language translator to break the hurdles present in the communication between hearing-impaired people and normal people. The developed system achieves 95.99% top-1 accuracy and 99.46% top-3 accuracy on the test dataset. The obtained results outperform the existing approaches using various deep models on different datasets.

APPROACHES AND SOLUTIONS FOR SIGN LANGUAGE RECOGNITION PROBLEM

2018 ◽  
Vol 5 ◽  
pp. 475-483
Author(s):  
Aleksejs Zorins ◽  
Pēteris Grabusts
Keyword(s):  
Neural Network ◽  
Sign Language ◽  
Recognition Problem ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Future Work ◽  
Artificial Neural Network Approach ◽  
Network Software

The goal of the paper is reviewing several aspects of Sign Language Recognition problems focusing on Artificial Neural Network approach. The lack of automated Latvian Sign Language has identified and proposals of how to develop such a system have made. Tha authors use analytical, statistical methods as well as practical experiments with neural network software. The main results of the paper are description of main Sign Language Recognition problem solving methods with Artificial Neural Networks and directions of future work based on authors’ previous expertise.

scholarly journals Indian Sign Language Recognition using Convolutional Neural Network

2021 ◽  
Vol 40 ◽  
pp. 03004
Author(s):  
Rachana Patil ◽  
Vivek Patil ◽  
Abhishek Bahuguna ◽  
Gaurav Datkhile
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hearing Ability ◽  
The People ◽  
Language Communication ◽  
Web Camera ◽  
Indian Sign Language

Communicating with the person having hearing disability is always a major challenge. The work presented in paper is an exertion(extension) towards examining the difficulties in classification of characters in Indian Sign Language(ISL). Sign language is not enough for communication of people with hearing ability or people with speech disability. The gestures made by the people with disability gets mixed or disordered for someone who has never learnt this language. Communication should be in both ways. In this paper, we introduce a Sign Language recognition using Indian Sign Language.The user must be able to capture images of hand gestures using a web camera in this analysis, and the system must predict and show the name of the captured image. The captured image undergoes series of processing steps which include various Computer vision techniques such as the conversion to gray-scale, dilation and mask operation. Convolutional Neural Network (CNN) is used to train our model and identify the pictures. Our model has achieved accuracy about 95%

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

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

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