Sign Language Detection of English Alphabets for Deaf and Dumb People
Raghav Jaju · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: This research explores the implementation of a robust system for sign language detection designed to facilitate communication for individuals who are deaf and dumb. Leveraging the capabilities of Python, Mediapipe, OpenCV, and Scikit Learn, our proposed system focuses on real-time hand sign recognition of English alphabets. The framework employs Mediapipe for hand landmark detection, enabling precise tracking of hand gestures. OpenCV is utilized for image processing, allowing efficient handling of video streams. The combination of these tools enables the extraction of relevant features from hand signs, forming the basis for our recognition model. The core of our system is built upon the Random Forest algorithm from Scikit Learn, implementing machine learning for the classification of hand signs. This approach ensures adaptability to various hand shapes and orientations, contributing to the versatility of the system. The proposed solution aims to bridge communication gaps for individuals with hearing and speech impairments, empowering them to express themselves through sign language. Through real-time detection and interpretation of hand signs, our system provides a valuable tool for enhancing communication and fostering inclusivity for the deaf and dumb community.