Classifying Sinhala Sign Language to Text Using Transfer Learning

Abstract

"Effective communication is essential for human beings to solve problems and address issues. Language is a crucial component of communication, and for people with hearing or speech impairments, sign language serves as a primary mode of communication. However, there is a lack of fully functional sign language recognition systems for minority languages such as Sri Lankan Sinhala sign language.

This research proposes a real-time Sinhala sign language recognition system that applies transfer learning techniques. Transfer learning involves reusing a pre-trained model on a different dataset to improve system performance."