IRO Journals
Journal of Ubiquitous Computing and Communication Technologies

Indian Sign Language Recognition and Translation System

Open Access
Published: 01 June, 2026
Pages: 145-160
  • Senthil Arun M. , Senthil Arun M.
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Suresh P. , Suresh P.
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Prakash K. , Prakash K.
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Syed Ibrahim A. , Syed Ibrahim A.
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Scholar, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Merlin Gethsy D. Merlin Gethsy D.
    Assistant Professor, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    Assistant Professor, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
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How to Cite

M., Senthil Arun, Suresh P., Prakash K., Syed Ibrahim A., and Merlin Gethsy D. 2026. “Indian Sign Language Recognition and Translation System”. Journal of Ubiquitous Computing and Communication Technologies 8 (2): 145-60. https://doi.org/10.36548/jucct.2026.2.006.
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Keywords

ISL - Indian Sign Language
BiLSTM
CNN
Sign Language Recognition
Sign Language Translation
Random Forest
Hand Tracking
Gesture Recognition

Abstract

The research focuses on overcoming the substantial communication barriers faced by deaf people in India because of the lack of professional sign language interpreters. The paper stresses the significance of ISL (Indian Sign Language) in enabling efficient communication in different sectors such as education and health care. The paper describes a system for recognizing and translating ISL that uses computer vision and machine learning methods. Specifically, a web camera is used to record hand gestures, which undergo the process of being tracked using MediaPipe framework. In addition, the system assesses the spatial and temporal aspects of hand movements by applying Random Forest, CNN, and BiLSTM models. Recognized signs are translated into text form, arranged in sentences and delivered through text-to-speech synthesizer. Thus, the web-based application does not need special hardware, which makes it more user-friendly. Experiments show that the system is able to recognize hand gestures and generate text and speech outputs in diverse environments thus helping to overcome the problem of communication gaps between deaf and hearing individuals. The efficacy of the comprehensive system test is excellent in practical terms; the Bi-LSTM model yielded an accuracy rate of 92.5% in dynamic word recognition, whereas the CNN models yielded an accuracy rate of 95.2% for alphabets and 96.3% for numerical gestures, and an excellent accuracy rate of 99.8% for static words, thus beating the accuracy benchmark of 81.0% set by conventional base reference systems.

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  11. Dataset: https://www.kaggle.com/datasets/satwikpasumarthi/indian-sign-language-recognition