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@article{180302,
        author = {Himanshu Tiwari and Alok Kumar and Radha Rani},
        title = {Hand Sign And Gesture Recoginition System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1078-1086},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180302},
        abstract = {Hand gestures constitute a significant non-verbal communication technique utilized in sign language. This method is predominantly employed by individuals with speech or hearing impairments to communicate with one another and with non-disabled persons. Although various sign language systems have been developed by multiple creators worldwide, these systems often lack adaptability and cost-effectiveness for end users. Consequently, this proposal presents a "Hand Sign and Gesture Recognition System Software" that offers a system prototype capable of automatically interpreting sign language, thereby facilitating more efficient communication among deaf and mute individuals, as well as with others. The system aims to bridge the communication gap between individuals who use sign language and those who do not, enabling smoother and more accessible interactions. This paper explores the development, methods, and applications of hand and foot recognition, focusing on the use of image processing, machine learning algorithms, and deep learning to improve the accuracy and uptime of the system. The proposed system is intended to be a flexible, cost effective way to enhance accessibility and communication capabilities for people with hearing and speech impairments, encouraging participation in a personal and professional environment. The ability to interpret and respond to human gestures enables more natural and intuitive communication with computers, devices, and machines. This field of research has garnered significant attention for applications in sign language translation, robotics, gaming, healthcare, and assistive technology. While gestures and hand signs have always been part of human communication, their effective recognition by machines is complex due to the variations in hand shapes, sizes, orientations, and environmental conditions. Recent advancements in machine learning, computer vision, and deep learning have enabled the development of more efficient and accurate gesture and sign recognition systems.},
        keywords = {Hand sign recognition, gesture recognition, machine learning, computer vision, human-computer interaction, deep learning, assistive technology.},
        month = {June},
        }