International Research Journal of Education and Technology

Sign language serves as a crucial mode of communication for the deaf and hard-of-hearing communities, offering a rich and expressive language that bridges individuals within these groups. However, the general unfamiliarity with sign language among the broader population poses significant barriers to effective interaction and social inclusivity. Indian Sign Language (ISL), widely utilized within India’s deaf community, is a distinct language characterized by its own grammar, syntax, and a complex range of hand gestures and facial expressions. Unlike spoken languages, ISL relies on visual cues, using both one-handed and two-handed gestures to convey meaning, and it often varies regionally, incorporating dialects that reflect local cultural nuances. The development of automated systems capable of recognizing ISL gestures can transform communication, enhancing interactions between sign language users and non-users and fostering inclusivity.

Despite its potential, the automated recognition of ISL gestures presents numerous challenges, primarily due to the variations in hand shapes, orientations, and movement patterns that make gesture recognition complex. To address these challenges, deep learning approaches have emerged as promising solutions, particularly Convolutional Neural Networks (CNNs). CNNs are adept at identifying spatial patterns within images, making them well-suited for capturing and analyzing the intricate nuances of hand movements and gestures present in sign languages. This project focuses on the development of a CNN-based model trained on a comprehensive, self-constructed dataset of ISL hand gestures. The dataset encompasses the diverse range of gestures intrinsic to ISL, enabling the model to learn and generalize complex patterns and variations inherent to the language. By accurately identifying ISL gestures, our model demonstrates the potential to bridge the communication gap between sign language users and the wider community, promoting greater social inclusion, accessibility, and understanding for the deaf and hard-of-hearing population in India. Through advancements in deep learning and gesture recognition, this project aspires to pave the way for more inclusive interactions and increased awareness of sign language as a vital form of communication.

Key Words: Indian Sign Language (ISL), Deaf Community,

Hand Gesture Analysis, Inclusivity, Automated ISL Recognition, Communication Barriers, Cultural Nuances in ISL.