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@article{165086,
        author = {Harsh kshirsagar and Prof. Jyotsna Nanajkar and Nikita Shinde and Aniket Shelke and Manoj Thombre},
        title = {Hand Gesture Recognition and Text Conversion Using Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {57-64},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165086},
        abstract = {Sign language is an important means of communication for people with speech disabilities, but it presents significant challenges for non-signers due to a widespread lack of interpreters and awareness. This paper explores the development of a hand sign understanding and translation system using convolutional neural networks (CNN) to bridge the communication gap between hearing and deaf communities. Our research focuses on a three-step methodology: data collection, model training and extensive evaluation. Using a custom CNN architecture, our system can detect and convert hand gestures into real-time text, providing a complete communication solution. The methodology includes a dataset specially curated for this purpose, and the training phase uses the MNIST dataset to initially calibrate the model. Our system demonstrates a remarkable 95.7% accuracy in recognizing the 26 letters of the American Sign Language (ASL) alphabet, demonstrating its potential to facilitate seamless communication between signers and non-signers. This advance highlight the promising application of deep learning methods to improve accessibility and inclusion in deaf and hearing communities.},
        keywords = {Sign Language, ASL, Hearing disability, Convolutional Neural Network (CNN), Computer Vision, Machine Learning, Gesture recognition, Sign language recognition, Hue Saturation Value algorithm.},
        month = {},
        }