Silent Expressions: Two-Handed Indian Sign Language Recognition Using MediaPipe and Machine Learning

Indian Sign Language (ISL) is an essential communication medium for individuals with hearing and speech impairments. This research introduces an efficient ISL recognition system that integrates deep learning with real-time hand tracking. Utilizing MediaPipe Hands for landmark detection and a Convolutional Neural Network (CNN) for classification, the model enhances recognition accuracy by incorporating two-hand detection. Additionally, pyttsx3 is used for speech synthesis, providing audio output for detected gestures. The system is designed to function in diverse environments, ensuring accessibility. Experimental evaluations demonstrate high accuracy, and the framework is adaptable for future enhancements, such as multi-language recognition and dynamic gesture interpretation.