Published Paper Details:

An ASL (American Sign Language) recognition system that employs machine learning is an innovative technology designed contribute to bridging the communication gap that exists between the environment and the deaf difficult of hearing persons. This system utilizes advanced algorithms and neural networks to analyze the intricate in body postures, facial gestures, and hand movements that make up sign language. By processing video or sensor data of sign language users, the machine learning model can identify and interpret these gestures, subsequently converting them into written text. The potential impact of such a system is profound. It enables those who use sign language as their primary mode of communication to interact with computers, smartphones, and other devices more seamlessly. Additionally, it facilitates better communication with individuals who do not understand sign language, as the system can bridge the language barrier by providing real-time translations.This technology promotes inclusivity and accessibility, ultimately enriching the lives of individuals with hearing impairments and fostering a more connected and understanding society. As machine learning and artificial intelligence continue to advance, so too does the potential for ASL recognition systems to become even more accurate and widespread in their applications.