Automated Sign Language Interpretation

Communication plays an essential role in human interaction, allowing individuals to express ideas and emotions. While spoken languages are widely used, individuals with hearing and speech impairments rely on sign language. However, the lack of widespread understanding of sign language creates communication barriers between them and the hearing community. This study presents a real-time Indian Sign Language (ISL) recognition system using the Media pipe framework and Long Short-Term Memory (LSTM) networks. The approach involves training an LSTM model to distinguish between different signs, utilizing a dataset generated through a pre-trained Holistic model from the Mediapipe framework, which serves as a feature extractor.