Real Time Sign Language Detection
Poorva Tupe, Afroz Sayed, Samiya Choudhary, Rupali Shelke, S Phani, Jiangpeng Dai, Jin Teng, Xiaole Bai, Zhaohui Shen, Dong Xuan, Pratiksha Bhuta, Karan Desai, Archita Keni, Abhishek Gupta, Shriram Ojha, Vikash Kumar, Vikrant Singh, Vipin Malav, Ramnagariya Gramothan
Abstract
Real-time sign language detection is an exciting and challenging project that aims to develop a system capable of recognizing sign language gestures and translating them into text or spoken language in real-time.This project can have a significant impact on the lives of people with hearing impairments by enabling them to communicate more easily with others who do not understand sign language .The project involves using computer vision and machine learning techniques to analyze video streams of sign language gestures and recognize the corresponding meanings.This requires building a dataset of sign language gestures, developing a deep learning model to train on this dataset, and integrating the model into a real-time application.The real-time application can be developed using a webcam or a mobile camera to capture video streams of the user's sign language gestures.The video streams can then be fed into the deep learning model to recognize the corresponding meanings, which can be displayed as text or spoken language.Overall, the real-time sign language detection project requires expertise in computer vision, machine learning, deep learning, and software development.However, the impact of this project can be significant, enabling people with hearing impairments to communicate more easily with others and improve their overall quality of life.