A Survey on Different Approach Used for Sign Language Recognition Using Machine Learning

Abstract

This paper presents a survey of different approaches used for sign language recognition using machine learning. Sign language recognition is a challenging problem that has attracted considerable research attention in recent years. Various machine learning techniques such as artificial neural networks, support vector machines, decision trees, and convolutional neural networks have been explored to recognize sign language gestures. The survey discusses the strengths and limitations of each approach, as well as their performance on different datasets. Moreover, it also discusses some recent advancements in sign language recognition, including the use of depth sensors and wearable devices. The survey concludes that while machine learning approaches have shown promising results, there is still room for improvement in terms of recognition accuracy, robustness to variations, and real-time performance.