Sign Language to Text Conversion using Random Forest
M. Jayalakshmi, Allu Pranathi Chowdari, Andra Gowthami, Akki Deepthika · 2025
In this study, a comprehensive approach for translating sign language into text using computer vision, machine learning, and real-time processing techniques is presented. The proposed system uses OpenCV for capturing the images and frames and Media Pipe is utilized for tracking hand movements and detection of landmarks. The landmarks extracted are processed and normalized then they are used as features for a Random Forest Classifier which is a machine learning algorithm well known for its classification accuracy. The process begins with capturing the images using the camera then the images are organizes into different categories representing numerous signs. By these images the hand landmarks are extracted and stored using Pickle for training process that happen later and data is being ready.In real-time scenario, the system collect the images, detect hand landmarks and classifies the images based on the landmarks.This research provide communication accessibility for the deaf and disabled community.