A Novel Deep Learning Approach for Real Time and Accurate Sign Language Translation

Sign language translation is a vital technology for facilitating communication between people who cannot understand sign language and others who have hearing impairments.. While several models have been developed for sign language translation, many face limitations such as inadequate accuracy, inability to handle complex signs, and lack of real-time translation capabilities. This paper presents a Sign language translation system designed help reduce the gap in communication between the deaf and hearing communities. The primary objective is to develop an efficient and accurate translator that creates animations in sign language from spoken language, making communication in real-time more accessible for those who are hard of hearing. The system combines Long Short-Term Memory networks for recognition of speech and Convolutional Neural Networks for image-based sign generation, achieving an impressive accuracy rate of 98%. Through Natural Language Processing (NLP), the recognized text is filtered and matched with predefined sign language phrases. If a match is found, the system plays an animation corresponding to the phrase; otherwise, it displays each letter individually. This dual-mode approach ensures flexibility in translation, catering to both common phrases and unique words. The high accuracy and adaptability of this system demonstrate its potential as a powerful instrument for improving accessibility and inclusion in daily communication. Results from experiments show that the suggested model performs better than current methods in terms of accuracy, robustness, and real-time performance. Through rigorous evaluation on benchmark datasets and user studies, the model achieves significant improvements in sign language translation accuracy, effectively closing the communication gap between the general public and those who are hearing impaired. In conclusion, the proposed sign language translation model represents a promising advancement in the field, offering enhanced accuracy and real-time capabilities compared to existing approaches. This technology has the potential to significantly increase the deaf and hard of hearing community’s accessibility and inclusivity in various contexts, including education, healthcare, and everyday communication.