Innovative Technology for Social Good: Real-Time Sign Language Generation Using TensorFlow
CNN-based gesture recognition; Communication barriers in India; Gesture recognition accuracy; Hand gesture recognition; Hearing impairment communication; Indian languages translation; Indian Sign Language (ISL); Pre-processed gesture data; Real-time sign language translation; Real-time video analysis; Sign language to text translation; TensorFlow Object Detection API; Transfer learning for gesture detection
Real-time sign language is a basic means of communication for hearing-impaired people. There is a substantial communication barrier between sign language users and those who cannot comprehend sign language. Indian Sign Language (ISL) is built to ease the social challenge between hearing-impaired people and individuals who are unable to understand sign language using TensorFlow featuring Indian languages for smoother communication. The study aims to build a proficient real-time sign language translator using TensorFlow to detect hand signals in real-time video streams. Integration of TensorFlow enables real-time gesture detection, demonstrating how technology can bring about real progress when it comes to improving communication with persons who cannot hear. The application is trained on a specialized dataset comprising different Indian sign language signals, pre-processed to improve gesture recognition focusing on fast and accurate sign language recognition and translation. The objective is to develop a model that can recognize and translate hand gestures into text in Indian languages. This approach uses TensorFlow object detection API to recognize body gestures from real-time videos. The model is trained on a unique dataset of diverse Indian languages that is pre-processed for better recognition accuracy. Techniques like transfer learning are employed to fine-tune the model by integrating CNN for gesture recognition. The detected outputs are after-ward transformed into Indian languages. The systems accuracy may be restricted because of the quality and variety of different Indian languages across the country. The findings indicate that the model can accurately translate the collection of sign languages into text highlighting the potential of TensorFlow Object Detection for real-time sign language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Kulkarni, Shreenidhi; Kaveramma, C. C. Nileem; Jayadurga, R.
Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.179-189
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-4880-1_15 https://www.scopus.com/pages/publications/105019297519?origin=resultslist
Kulkarni S., CHRIST University, Karnataka, Bengaluru, India; Kaveramma C.C.N., CHRIST University, Karnataka, Bengaluru, India; Jayadurga R., CHRIST University, Karnataka, Bengaluru, India