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@article{205846,
        author = {Saniya Pathan and Swaraj Nangare and Riya Patil and Sakshi Renuse and Anagha Chaphadkar},
        title = {A Real-Time Indian Sign Language Recognition System Using MediaPipe Hand Landmarks and Random Forest Classification for Multilingual Assistive Communication},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8415-8420},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205846},
        abstract = {This work presents a real-time sign language recognition system developed to improve communication for individuals with hearing and speech impairments. The system uses MediaPipe to detect hand landmarks and applies a Random Forest–based machine learning model to classify gestures. Instead of relying on computationally intensive deep learning methods, the approach focuses on normalized landmark features, enabling efficient processing and faster execution on standard hardware. The application is implemented in Python and includes an interactive graphical interface built with Tkinter. It supports continuous gesture recognition, sentence generation, speech output, and translation into regional languages such as Hindi and Marathi. The proposed system was evaluated using a dataset of 1500 gestures samples and achieved an accuracy of 96.2%, precision of 95.8%, recall of 95.5%, and F1-score of 95.6%. The proposed system demonstrates the feasibility of deploying lightweight machine learning models for assistive communication applications without requiring specialized hardware. Experimental results indicate that the approach provides an efficient and scalable solution for real-time sign language interpretation.},
        keywords = {Sign Language Interpreter, MediaPipe, Random Forest, Machine Learning, Gesture Recognition, Assistive Communication, Indian Sign Language.},
        month = {June},
        }