ITM Web Conf.
Volume 40, 2021International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
|Number of page(s)||5|
|Published online||09 August 2021|
Indian Sign Language Recognition using Convolutional Neural Network
Ramrao Adik Institute Of Technology, Nerul, India
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%
Key words: Convolutional Neural Network(CNN) / Hand Gesture / Deaf people / Sign Language / Sign language Recognition(SLR) / ISL(Indian Sign Language)
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.