Real-Time Sign Language Detection using TensorFlow, OpenCV and Python

Author:

Verma Prashant,Badli Khushboo

Abstract

Abstract: Deaf and hard-of-hearing persons, as well as others who are unable to communicate verbally, utilise sign language to communicate within their communities and with others. Sign languages are a set of preset languages that communicate information using a visual-manual modality. The dilemma of real-time finger-spelling recognition in Sign Language is discussed. We gathered a dataset for identifying 36 distinct gestures (alphabets and numerals) and a dataset for typical hand gestures in ISL created from scratch using webcam images. The system accepts a hand gesture as input and displays the identified character on the monitor screen in real time. This project falls under the category of human-computer interaction (HCI) and tries to recognise multiple alphabets (a-z), digits (0-9) and several typical ISL hand gestures. To apply Transfer learning to the problem, we used a Pre-Trained SSD Mobile net V2 architecture trained on our own dataset. In the vast majority of situations, we constructed a robust model that consistently classifies Sign language. Many studies have been done in the past in this area employing sensors (such as glove sensors) and other image processing techniques (such as edge detection technique, Hough Transform, and so on), but these technologies are quite costly, and many people cannot afford them. During the study, various human-computer interaction approaches for posture recognition were investigated and evaluated. The optimum solution was determined to comprise a set of image processing approaches with Human movement categorization. Without a controlled background and low light, the system can detect chosen Sign Language signs with an accuracy of 70-80%. As a result, we're creating this software to assist such folks because it's free and simple to use. However, aside from a small group of people, not everyone is familiar with sign language, and they may need an interpreter, which may be cumbersome and costly. This research intends to bridge the communication gap by building algorithms that can anticipate alphanumeric hand motions in sign language in real time. The main goal of this research is to create a computer-based intelligent system that will allow deaf persons to interact effectively with others by utilising hand gestures. Keywords: Pre-Trained SSD Mobile net V2, Sign Language , HCI

Publisher

International Journal for Research in Applied Science and Engineering Technology (IJRASET)

Subject

General Earth and Planetary Sciences,General Environmental Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sign Language Recognition Using OpenCV and Convolutional Neural Networks;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3