Mediapipe and CNNs for Real-Time ASL Gesture Recognition
This incremental work addresses communication for people with hearing impairments by improving gesture recognition accuracy.
The paper tackled real-time recognition of American Sign Language (ASL) gestures using Mediapipe for feature extraction and a CNN for classification, achieving 99.95% accuracy on ASL alphabets.
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.