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A system designed to convert hand sign gestures into voice and text using a Python- based Convolutional Neural Network (CNN) model offers a robust solution for bridging communication gaps. This system leverages computer vision techniques to capture hand gestures, which are then processed by a trained CNN model for recognition. The recognized gestures are subsequently converted into textual output and further synthesized into speech using text-to-speech (TTS) libraries. This approach provides a real- time, accessible method for individuals using sign language to communicate with those unfamiliar with it, enhancing inclusivity and understanding.