Real time sign language translator

Mohd Nazri, Muhamad Hairol Ikram (2022) Real time sign language translator. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Speech impairment is a disability that affects an individual’s ability to communicate using speech and hearing. People who are affected by this use other media of communication such as sign language. Although sign language is ubiquitous in recent times, there remains a challenge for non-sign language speakers to communicate with sign language speakers or signers. With recent advances in deep learning and computer vision, there has been promising progress in the fields of motion and gesture recognition using deep learning and computer vision-based techniques. In recent years, deep learning has been used in image classification, object tracking, action recognition, and scene labeling. Traditionally, Image Processing techniques were used to solve any Computer Vision problems that occurred in an artificial intelligence system. However, in real-time identification, image processing cannot be used. This is where Deep Learning concepts are applied. The focus of this work is to create a real time sign language translator which offers sign language translation to text thus aiding communication between signers and non-signers. We built a simple Convolutional Neural Network for object detection. The model is trained, and multiple test cases are implemented in the TensorFlow environment to obtain accurate results.

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