International Journal For Multidisciplinary Research

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Real-Time ISL Recognition Using CNN and MediaPipe

Author(s) Sakshi Huse, Rohini Makode, Tejas Nachane, Tejas Wankhade
Country India
Abstract Communication barriers often isolate the deaf and mute community due to a lack of universal understanding of sign language, limiting their ability to interact effectively in society. This project introduces a deep learning-based real time hand gesture recognition system to address this challenge by acting as a digital translator for hearing and speech-impaired individuals. Using MediaPipe for robust hand landmark detection and a custom-trained Convolutional Neural Network (CNN), the system accurately recognizes single and dual-hand gestures across 36 classes, including alphanumeric characters (0-9, A-Z). The system ensures high accuracy and real-time performance through GPU
acceleration, enabling applications in online communication, virtual classrooms, touchless interaction, and Human Computer Interaction (HCI). By bridging the communication gap, it fosters inclusivity, improves accessibility, and enhances social connectivity for individuals with hearing and speech impairments. Furthermore, the scalable design allows the system to be expanded for additional gestures and languages, paving the way for broader adoption in
educational, professional, and assistive technology contexts, ultimately empowering individuals and enabling a more connected and inclusive society.
Keywords Machine Learning, CNN, Sign Language, Python, TensorFlow, Mediapipe
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-08
Cite This Real-Time ISL Recognition Using CNN and MediaPipe - Sakshi Huse, Rohini Makode, Tejas Nachane, Tejas Wankhade - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35983
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.35983
Short DOI https://doi.org/g84fdm
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