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@article{169250,
        author = {Ms.Prakriti Jain and Mr.Vikas G Singh and Mr.Onkar Bhute and Mr.Shreeyash Bachal and Mr. Shreyas Janbandhu and Mr. Shreyash Kotangale and Mr. Virag Jambhore},
        title = {SignSpeak: Indian Sign Language to Text Convertor},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {558-563},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169250},
        abstract = {Indian Sign Language (ISL) serves as a vital mode of communication for the deaf and hard-of-hearing community in India. However, the lack of widespread understanding of ISL among the general population creates communication barriers. This project presents an "Indian Sign Language to Text Converter," a real-time system designed to bridge this gap by translating ISL gestures into readable text. The system leverages computer vision and machine learning techniques to recognize hand gestures and convert them into corresponding text. Using Python, OpenCV, TensorFlow, and Keras, the project implements a deep learning model for gesture recognition. A custom dataset of ISL gestures was created, involving image preprocessing techniques such as background subtraction, edge detection, and skin color segmentation to enhance accuracy. Real-time video input allows dynamic sign detection, with the system optimized for low-latency performance. The system aims to foster inclusivity and improve communication by providing an accessible tool for ISL users and non-signers alike.},
        keywords = {Machine Learning Algorithms, RNN, CNN, Indian sign language, Gesture recognition.},
        month = {November},
        }