Review of Real-Time Sign Language Detection, Speech Output Using Esp32-Cam and Wi-Fi

Sign language detection has emerged as a transformative technology aimed at bridging the communication gap between hearing-impaired and non-signing individuals. This paper presents a review of a real-time sign language interpretation system that uses computer vision and deep learning techniques to recognize hand gestures performed in front of a camera and convert them into corresponding text and speech. The proposed framework integrates image processing with convolutional neural networks (CNNs) to accurately classify gestures from live video input. Furthermore, a text-to-speech (TTS) module is incorporated to vocalize the recognized words, enabling seamless two-way communication. The implementation demonstrates how artificial intelligence (AI) can facilitate inclusivity by translating visual gestures into natural language, making interactions more accessible for the deaf and mute community. This review highlights the technical workflow, societal impact, and future potential of sign language detection systems in promoting human-computer interaction and assistive technology.