Development of a PC-based sign language translator

Abstract

While a hearing-impaired individual depends on sign language and gestures, non-hearing-impaired person uses verbal language. Thus, there is need for means of arbitration to forestall situation when a non-hearing-impaired individual who does not understand the sign language wants to communicate with a hearing-impaired person. This paper is concerned with the development of a PC-based sign language translator to facilitate effective communication between hearing-impaired and non-hearing-impaired persons. Database of hand gestures in American sign language (ASL) is created using Python scripts. TensorFlow (TF) is used in the creation of a pipeline configuration model for machine learning of annotated images of gestures in the database with the real time gestures. The implementation is done in Python software environment and it runs on a PC equipped with a web camera to capture real time gestures for comparison and interpretations. The developed sign language translator is able to translate ASL/gestures to written texts along with corresponding audio renderings at an average duration of about one second. In addition, the translator is able to match real time gestures with the equivalent gesture images stored in the database even at 44% similarity.

Development of Mobile Application for Sign Language Translation Using Deep Learning Technique

“…However, in recent time, researchers are now focusing on sign language translation, some of these researches are reviewed in this section so as to know the stateof-art in sign language translation. A PC-based sign language based translator was developed by [7] using Python and TensorFlow. It translates ASL gestures to written texts with audio renderings in about one second and can match real-time gestures with equivalent gesture images at 44% similarity.…”