A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognition

TL;DR

This paper presents a transformer-based multi-stream model for recognizing Iranian Sign Language, achieving over 90% accuracy and enabling real-time feedback to improve sign language learning and communication for the deaf community.

Contribution

It introduces a novel multi-stream transformer approach with genetic algorithm optimization and multi-task learning for Iranian Sign Language recognition.

Findings

Achieved 90.2% accuracy on test data.

Demonstrated effective real-time sign language recognition.

Showed positive impact of feedback in sign language learning software.

Abstract

Sign language is an essential means of communication for millions of people around the world and serves as their primary language. However, most communication tools are developed for spoken and written languages which can cause problems and difficulties for the deaf and hard of hearing community. By developing a sign language recognition system, we can bridge this communication gap and enable people who use sign language as their main form of expression to better communicate with people and their surroundings. This recognition system increases the quality of health services, improves public services, and creates equal opportunities for the deaf community. This research aims to recognize Iranian Sign Language words with the help of the latest deep learning tools such as transformers. The dataset used includes 101 Iranian Sign Language words frequently used in academic environments such as universities. The network used is a combination of early fusion and late fusion transformer encoder-based networks optimized with the help of genetic algorithm. The selected features to train this network include hands and lips key points, and the distance and angle between hands extracted from the sign videos. Also, in addition to the training model for the classes, the embedding vectors of words are used as multi-task learning to have smoother and more efficient training. This model was also tested on sentences generated from our word dataset using a windowing technique for sentence translation. Finally, the sign language training software that provides real-time feedback to users with the help of the developed model, which has 90.2% accuracy on test data, was introduced, and in a survey, the effectiveness and efficiency of this type of sign language learning software and the impact of feedback were investigated.