Sign Language Detection Using Gloves

This paper presents a comprehensive system for real-time translation of Indian Sign Language (ISL) gestures into spoken language using gloves equipped with flex sensors. The system incorporates an Arduino Nano microcontroller for data acquisition, an HC-05 Bluetooth module for wireless data transmission, and an Android application for processing. A deep learning model, trained on an ISL dataset using Keras and TensorFlow, classifies the gestures. The processed data is then converted into spoken language using Google Text-to-Speech (GTTS). The gloves measure finger movements through flex sensors, with data transmitted to the Android app for real-time classification and speech synthesis. This system is designed to bridge communication gaps for the hearing-impaired community by providing an intuitive and responsive translation tool. Our evaluation shows high accuracy in gesture recognition, with average latency ensuring near real-time performance. The system's effectiveness is demonstrated through extensive testing, showcasing its potential as an assistive technology. Future improvements include expanding the dataset and incorporating additional sensors to enhance gesture recognition accuracy and robustness. This research highlights the integration of wearable technology and machine learning as a promising solution for enhancing accessibility and communication for sign language users