Sign Language Capture and Illustration System with Sensor Integration

  • Unique Paper ID: 196435
  • Volume: 12
  • Issue: 11
  • PageNo: 4235-4243
  • Abstract:
  • Sign language plays a crucial role in communication for hearing and speech-impaired individuals, while conventional communication methods relying on human interpreters or camera-based systems are often costly, environment-dependent, and unsuitable for real-time or continuous interaction. This paper presents an intelligent sign language capture and illustration framework that integrates sensor technology, embedded systems, and machine learning techniques. Hand gestures captured using a wearable glove embedded with flex sensors are analyzed for accurate gesture recognition. The sensor data is processed using a microcontroller and classified using machine learning algorithms for real-time interpretation. The recognized gestures are converted into readable text and visual illustrations to facilitate effective communication. The system is designed to be portable, cost-effective, and independent of lighting conditions. A real-time processing approach ensures low latency and quick response. Experimental results demonstrate improved recognition accuracy, faster response time, and reduced dependency on external environmental conditions compared to traditional approaches. The proposed framework supports seamless communication, enhances accessibility, and improves the quality of life for individuals with hearing and speech impairments, contributing to assistive technology and inclusive smart systems.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{196435,
        author = {Jeevanthini J and Atchaya shree E and Kiruba N and Mrs. Velvizhi L},
        title = {Sign Language Capture and Illustration System with Sensor Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4235-4243},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196435},
        abstract = {Sign language plays a crucial role in communication for hearing and speech-impaired individuals, while conventional communication methods relying on human interpreters or camera-based systems are often costly, environment-dependent, and unsuitable for real-time or continuous interaction. This paper presents an intelligent sign language capture and illustration framework that integrates sensor technology, embedded systems, and machine learning techniques. Hand gestures captured using a wearable glove embedded with flex sensors are analyzed for accurate gesture recognition. The sensor data is processed using a microcontroller and classified using machine learning algorithms for real-time interpretation. The recognized gestures are converted into readable text and visual illustrations to facilitate effective communication. The system is designed to be portable, cost-effective, and independent of lighting conditions. A real-time processing approach ensures low latency and quick response. Experimental results demonstrate improved recognition accuracy, faster response time, and reduced dependency on external environmental conditions compared to traditional approaches. The proposed framework supports seamless communication, enhances accessibility, and improves the quality of life for individuals with hearing and speech impairments, contributing to assistive technology and inclusive smart systems.},
        keywords = {Sign Language Recognition, Flex Sensors, Machine Learning, Embedded Systems, Gesture Detection, Real-Time Processing, Assistive Technology, Smart Communication},
        month = {April},
        }

Cite This Article

J, J., & E, A. S., & N, K., & L, M. V. (2026). Sign Language Capture and Illustration System with Sensor Integration. International Journal of Innovative Research in Technology (IJIRT), 12(11), 4235–4243.

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