• Sohan Wasker
• Atharva Kanse
• Aman Vashishtha
• Harshavardhan Latkar
• Prof. Supriya Agre

This research focuses on the development of a real-time system for the conversion of sign language into text and speech, and vice-versa, to facilitate seamless communication between individuals with hearing impairments and those without. The proposed system integrates advanced technologies such as Convolutional Neural Networks (CNN), image processing, and animation gesture recognition to achieve accurate and efficient translation between sign language and spoken language. The use of CNN plays a pivotal role in the recognition of sign language gestures from video input. The model is trained on a diverse dataset of sign language gestures, allowing it to learn and generalize the intricate hand movements and expressions inherent in sign language communication. This deep learning approach enhances the system's ability to recognize a wide range of gestures with high accuracy. Image processing techniques are employed to preprocess the video input, extracting relevant features and reducing noise to enhance the overall performance of the system. The integration of image processing not only contributes to the accuracy of gesture recognition but also ensures robustness in varying lighting conditions and background environments. A distinctive feature of the system lies in the incorporation of animation gesture recognition, which involves simulating real gestures for recognition. This simulation not only aids in accurately capturing the nuances of sign language gestures but also enhances the overall user experience. The system utilizes these animated representations for recognition and subsequently converts them into text or speech, providing a dynamic and expressive layer to the communication process. The real-time nature of the system ensures minimal latency in the translation process, enabling instantaneous communication between individuals using sign language and those relying on spoken language. The proposed solution holds promise in breaking down communication barriers and fostering inclusivity in various social and professional settings. Sign Language, Convolutional Neural Network (CNN), Image processing, Natural Language Processing (NLP), Text-to-Speech (TTS)

Sign Language, Convolutional Neural Network (CNN), Image processing, Natural Language Processing (NLP), Text-to-Speech (TTS)

"Real-Time Conversion Of Sign Language To Text And Speech And Vice-Versa", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d252-d255, November-2023, Available :http://www.jetir.org/papers/JETIR2311332.pdf

"Real-Time Conversion Of Sign Language To Text And Speech And Vice-Versa", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd252-d255, November-2023, Available at : http://www.jetir.org/papers/JETIR2311332.pdf