  This research paper describes a realtime system for identifying American Sign
Language (ASL) movements that employs modern computer vision and machine
learning approaches. The suggested method makes use of the Mediapipe library
for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture
classification. The testing results show that the suggested system can detect
all ASL alphabets with an accuracy of 99.95%, indicating its potential for use
in communication devices for people with hearing impairments. The proposed
approach can also be applied to additional sign languages with similar hand
motions, potentially increasing the quality of life for people with hearing
loss. Overall, the study demonstrates the effectiveness of using Mediapipe and
CNN for real-time sign language recognition, making a significant contribution
to the field of computer vision and machine learning.
