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@article{185812,
        author = {Snehal Abhijeet Gaikwad and Dr Virendra Shete},
        title = {Survey on Border Coolie Optimization Technique for Hand Gesture Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {99-107},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185812},
        abstract = {Hand gestures are observed as an effective tool for making the interaction in the community with individuals having intellectual disabilities. It is highly essential for communicating the computers and people. Therefore, it is aimed to design an automatic hand gesture recognition approach that is utilized for repeatedly performing human-computer interaction. Sign languages are considered the natural languages utilized by hearing-impaired people that involve some expressional way of communication in routine life. It reveals the sentences, words, and letters present in the spoken language for performing the gesticulations that enable communication between them. The deaf community makes communicates with normal people using an automation system that relates the signs with the words of speech. The hand gesture recognition system is implemented independently of requiring any unique hardware rather than using the webcam. Thus, it is highly significant to make a short review of hand gesture recognition based on deep learning techniques considering the Indian sign language. Hence, this paper discusses and clarifies existing research work based on hand gesture recognition in Indian sign language with algorithmic classification. This survey also compares different performance measures, datasets utilized, and also different tools used for the implementation. Then, upcoming research and also current research gaps in hand gesture recognition in Indian sign language are analyzed. This review on state-of-the-art hand gesture recognition for Indian sign language tools has shown their potential for providing the right solution in different real-life situations. It is hoped that the contents and illustrations in this paper assist researchers in laying a good foundation to inform their studies.},
        keywords = {Hand Gesture Recognition; Indian Sign Languages; Deep Learning Techniques; Algorithmic Categorization; Performance Metrics; Dataset Details; Implementation Tools},
        month = {},
        }