SignSpeak: Exploring the Sign Language Communication Using Deep Neural Networks

This research’s primary goal is to create a system for detecting sign language. Sign language has long been accepted as a genuine mode of communication for people who are having hearing-impaired problems. The inability of technology to detect and comprehend sign language has limited its usage in daily life. This research will look at many ways to sign language detection, such as image and video processing using deep learning approaches. The objective of the research work is to develop a system that can recognize and interpret sign language properly, with the potential to increase accessibility and inclusion so that it supports hearing-impaired people. The study’s findings will be examined through a series of tests and assessments, and the system’s possible applications and future advances will be explored. This paper will give an idea of how deaf and dumb people can engage in utilizing web platforms (video calls, meets, etc). Assisting them in expressing their thoughts, particularly while using internet platforms. In this paper, we are using the Hybrid CNN-LSTM models that have been presented as a means to increase performance on tasks such as image recognition by combining the resilience of both networks Convolutional Neural Networks (CNNs) for sign gesture recognition and for predicting the equivalent words uses Long Short-Term Memory (LSTM). The role CNNs is to extract features from pictures and other data with grid-like layouts, but LSTMs excel at modelling sequential data while maintaining context. A hybrid CNN-LSTM model can take use of the capabilities of both networks by merging them, possibly leading to higher performance on a number of applications. A hybrid CNN-LSTM model may be implemented by using a CNN to extract features from an input imagery and then feeding those features into an LSTM network to simulate the data’s sequential structure. Another method is to employ CNNs and LSTMs in simultaneously, with the CNN processing spatial data and the LSTM processing temporal data. The proposed approach has been assessed using three benchmark multilingual datasets, including RWTH-PHOENIX-Weather 2014T, How2Sign, and ISL-CSLRT and justifies the significance of the proposed work by achieving 98% recognition accuracy and reduced word error rate.