Sign Language Detection Using CNN and LSTM Based Model

Abstract

In the context of our current work, we have taken a major step in examining and contrasting the effectiveness of several machine learning models for sign language recognition. Convolutional Neural Network (CNN) model building was the focus of our minor project, which resulted in the successful publication of a research paper [6]. Our method was validated by this model, which showed significant accuracy in recognizing sign language. We wanted to build a Long Short-Term Memory (LSTM) model for the same reason as we moved on to our larger project. The aim was not only to construct an additional efficient model but also to conduct a comparative analysis between the LSTM and the CNN model that was previously constructed. This comparison included a thorough assessment of both models based on a number of variables, including accuracy, loss, speed, and others. We can now confidently state that we have created an LSTM model for sign language recognition that is fully operational. In addition, we have compared our CNN model with our LSTM model, assessing both models according to training accuracy, training loss, validation accuracy, validation loss, and training duration. This development opens the door for more research in this area and represents an important turning point in our study. Our research will be strengthened by the solid foundation our findings provide. Keywords: Convolutional Neural Network, Long Short-Term Memory, Sign Language Detection, Training, Validation