Ego-SLD: A Video Dataset of Egocentric Action Recognition for Bengali Sign Language Detection

Sign Language Recognition integrates computer vision and natural language processing to automatically interpret hand gestures and translate them into spoken or written Bengali. The primary goal is to bridge the communication gap between sign language users and non-users by recognizing gestures, movements, postures, and facial expressions that correspond to spoken language elements. Since hand gestures are the cornerstone of sign language communication, they play a pivotal role in improving the accuracy of sign language recognition systems. This article introduces Ego-SLD, a video dataset specifically designed for Egocentric Action Recognition of Bangla Sign Language used in daily life situations. The dataset contains videos of 16 commonly used words, collected from 12 individuals (10 males and 2 females) aged between 16 and 42 years. Each participant provided two samples of each word in an indoor environment with standard lighting conditions. Ego-SLD is a crucial resource for the automatic recognition of Bangla sign language and holds significant potential to benefit the deaf community. Moreover, it serves as a valuable dataset for researchers focusing on vision-based sign language detection, hand gesture recognition, and egocentric action recognition.