An Improved Artificial Intelligence Assisted Sign Language Recognition Methodology Using Modified Deep Learning Principle

Sign language is the primary mode of communication for millions of people worldwide, particularly for those who are deaf or hard of hearing. However, the lack of widespread knowledge of sign language within the general population often creates barriers to effective communication. This may be addressed by AI-driven automated sign language recognition (SLR) systems that allow for real-time translation between sign language users and non-users. Using a tweaked version of the Deep Learning (DL) concept, this research details a new approach to AI-assisted SLR. A hybrid architecture is used by the system to extract spatial features, analyze temporal sequences with Long Short-Term Memory (LSTM) networks, and improve accuracy by focusing on critical areas of the input with attention processes. The model outperforms previous models with an impressive accuracy of 98.33% after being trained and evaluated on the Sign Language MNIST dataset. This high level of accuracy, coupled with its computational efficiency, makes the suggested methodology ideal for real-time sign language interpretation systems. The methodology has the potential to significantly improve accessibility and communication for the hearing-impaired population. Future work can extend the system's capabilities to handle dynamic gestures and expand its applicability to a wider range of sign languages.