Sign Language Recognition using Convolutional Neural Network
Nancy Gulati, Abhishek Rajput, Ankur Singh · 2024
The Sign Language Recognition (SLR) sector has seen significant progress in recent years, driven by the growing need for technology to bridge the communication gap for the deaf and hard-of-hearing community. This research paper synthesizes the methods, results, and limitations of some prominent influential articles in the field of SLR, highlighting the state of the art and remaining challenges. By consolidating knowledge from diverse research efforts, this research provides a comprehensive understanding of the current landscape and is a valuable resource for researchers, practitioners, and stakeholders committed to promoting accessibility and inclusion for deaf people. This research also highlights work done on the Sign Language model using Convolutional Neural Network(CNN) along with an average accuracy of 99.98 percent.