Gesture-to-Text: A Real-Time Indian Sign Language Translator with Pose Estimation and LSTMs
Shubham Shetty, Ebrahim Hirani, Abhir Singh, Reeta Koshy
Abstract
In recent years, there have been notable advancements in technology, deep learning, and pose detection. One significant application of these advancements pertains to the real-time detection of sign language from video sources. The motivation behind this research stems from the pressing societal need to enhance the quality of life for individuals with speech impairments. Given the current prominence of online meetings, exacerbated by the COVID-19 pandemic, there is a growing need for systems that can provide individuals with speech impairments greater independence in communication, eliminating the requirement for a human translator. This research proposal advocates for a solution that leverages PoseNet algorithms for the extraction of key pose points, which are subsequently employed within LSTM models for the predictive modeling of sign language gestures. This research paper aims to make several notable contributions to the field of assistive technology and human-computer interaction. The achieved accuracy stands at an impressive 98%, underscoring the robustness and precision of our proposed system.