Advancements in Vision-Based Sign Language Recognition: A Comprehensive Review

Authors

  • Dinushika Chithrani Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka
  • Adithya Rajapakshe Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka
  • Dhanuka Jayasinghe Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka
  • Umaya Balagalla Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka
  • Bhathiya Pilanawithana Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka
  • Uditha Wijewardhana Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka
  • Udaya Wijenayake Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka

DOI:

https://doi.org/10.31357/contre.v1i1.7392

Keywords:

Sign Language, Gesture recognition, Computer Vision, Machine Learning, Image Processing

Abstract

As nearly 70 million individuals suffer from disabling hearing loss, sign language serves as an important means of communication. Unfortunately, the lack of proficiency in sign language among the general population hinders meaningful interactions with those who rely on it. This paper presents an extensive analysis of the cutting-edge methodologies in sign language translation, with the ultimate goal of facilitating effective communication between sign language users and the broader community. In addition to reviewing state-of-the-art approaches, this work also investigates into the challenges and limitations faced by gesture recognition research. Overall, it is expected that the study may provide readers and researchers with a guide for future research and creation in the field of sign language recognition.

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Published

2024-05-02