Sign Language Interpreter using Deep Learning

Abstract Sign languages constitute a family of rich, expressive visual languages used by deaf and hard-of-hearing communities around the world. A lack of trained human interpreters creates communication barriers for the roughly seventy million deaf people globally, who collectively employ more than 300 distinct sign languages. Recent advances in computer vision and deep learning have spurred research into automated sign language recognition systems capable of translating hand gestures into spoken or written language. This paper provides a concise yet comprehensive technical evaluation of the open-source project Sign Language Interpreter using Deep Learning and situates it within the broader landscape of sign language recognition. We describe the project’s goals, data collection and pre-processing pipeline, convolutional neural network architecture, training procedure, and performance. We relate these elements to existing research, highlight unique contributions such as an evaluation framework and ethical analysis, and discuss limitations and future improvements. Citations from the United Nations and peer-reviewed literature ground our discussion in external facts. By distilling a larger report into a focused narrative, this paper aims to serve as a bridge between open-source prototypes and the academic community.