Recognition of Gujarati Sign Language Alphabets Using LSTM Deep Learning Approach

Abstract

Worldwide sign languages differ, and there isn't one universal sign language. Each country and state may have its sign language or set of related sign languages. Some of the previous research studies recognized the signs, but they required instruments like gloves, sensors, and kinetics, or many other hardware instruments that are not easily accessible for everyone. In this modern era, cameras are widely used or easily accessible to everyone. Recognition of Gujarati alphabet sign language with a camera presents a cost-effective technique to detect Gujarati alphabet signs. This research contains data acquisition, image pre-processing, feature extraction, and sign recognition. Data are collected from images taken from different people at different angles with signs. Augmentation of Data technique is also used to increase the sample size of the dataset. The model which is proposed is used as a long short-term network to translate sign language with around 98% accuracy. This study contributes to the development of an effective human-machine solution for the deaf society.