Efficient Development of Gesture Language Translation System using CNN

Gesture Language Translation System (GLTS) is an interdisciplinary research field that intersects computer vision, machine learning, also human-machine interaction. This study offers a extensive exploration regarding current technologies, methodologies, and challenges in GLTS. The paper explores the evolution of sign communication recognition systems and their applications across diverse domains, such as human-computer interaction, virtual reality, and healthcare. Particular emphasis is given to sign communication translation, an emerging concept facilitating the translation of gestures into meaningful language, thereby enabling more intuitive communication. Challenges associated with translating gestures into language are discussed, and potential solutions are explored to enhance the accuracy and efficiency of GLTS. The research underscores the real-world impact of GLTS, showcasing its potential to revolutionize human-machine interactions across various contexts. The inclusion of case studies and examples of successful GLTS implementations provides practical insights and lessons learned from these instances. The methodology section details the research approach, encompassing the dataset, tools, and algorithms utilized for evaluating GLTS system effectiveness. The presentation and analysis of experimental results, along with relevant metrics and performance evaluations, form a critical component of this study. The discussion part analyses the results in light of the body of previous research, considers field implications, and suggests possible directions for further study and advancement.