ISL Smart Communicator: An Intelligent System for Bridging the Communication Gap

Sign language is one of the most important communication methods used by deaf and verbally disabled individuals in their daily lives. However, communication between these people and the public often becomes difficult because most people are not familiar with sign language gestures. According to global disability statistics, millions of individuals rely on sign language as their primary mode of communication, highlighting the urgent need for intelligent assistive technologies. This paper proposes ISL Smart Communicator, a smart communication system designed to bridge the interaction gap between mute individuals and non-sign language users by recognizing Indian Sign Language (ISL) gestures and converting them into understandable text and speech in real time. The system utilizes computer vision and deep learning techniques to capture and process hand gestures through a camera. Image preprocessing techniques such as histogram equalization are applied to enhance visual quality, while feature extraction methods including contour tracing, Discrete Wavelet Transform, and Grey Level Co-occurrence Matrix (GLCM) are employed to improve gesture recognition accuracy. The proposed solution is developed using a modern AI technology stack including Python, TensorFlow/Keras, TensorFlow Lite, OpenCV, MediaPipe, NumPy, Pandas, scikit-learn, Matplotlib, Seaborn and pyttsx3 for gesture recognition, model training, and text-to-speech generation.