SmartSign: Real-Time Hand Gesture Recognition and Speech Generation Platform

Abstract

Sign language is an essential means of communication for individuals with hearing and speech impairments. However, communication barriers often arise between sign language users and people who are unfamiliar with sign language. To address this challenge, this paper presents a SmartSign: Real-Time Hand Gesture Recognition and Speech Generation Platform that uses computer vision and deep learning techniques to recognize hand gestures and convert them into understandable text. The proposed system is developed using Python, Flask, OpenCV, MediaPipe, and a trained machine learning model. It provides multiple detection methods, including image-based detection, video-based detection, multi-image processing, and real-time webcam recognition. The system also incorporates interactive learning features such as sign language practice, tutorials, a chatbot, progress tracking, history, and analytics to support continuous learning. Additionally, an administrative panel enables administrators to manage users, tutorials, detection history, and application settings. The system processes hand landmarks and extracts relevant features to identify corresponding signs and display the predicted results through a user-friendly web interface. By integrating sign recognition with learning and progress-monitoring facilities, the proposed application aims to improve accessibility, support sign language education, and reduce communication barriers. The system demonstrates the potential of combining computer vision, deep learning, and web technologies to develop an interactive and accessible sign language recognition platform