International Journal of Image Processing and Pattern Recognition

Review on Sign Language Detection Using Action Detection

  1. Smriti Shaji Nair ✉

Abstract

The development of sign language as a means of communication for those with hearing loss has been a significant step. Sign language is always needed for communication as not everyone understands how to translate signs. Without an interpreter, it is challenging to communicate. The hand shape, motion profile, and positioning of the hand, face, and other body components vary amongst signs in a particular sign language. Therefore, visual sign language detection is a difficult area of computer vision research. To overcome this, we need a flexible and long-lasting solution. For barrier-free communication, sign language must be translated so that it may be utilised by the broader population. The two primary methods for detecting sign language are image-based and sensor-based strategies. An image-based solution employs one or more cameras to record a series of images of the signer making the sign, which are then processed by image recognition software to identify the sign. The sensor-based approach tracks the hand articulations using instrumental gloves that have sensors built in. This project's primary goal is to break down barriers that separate the deaf and dumb from the rest of society

References (13)

  1. Shirbhate RS, Shinde VD, Metkari SA, Borkar PU, Khandge MA. Sign language Recognition Using Machine Learning Algorithm. Int Res J Eng Technol (IRJET). 2020 Mar; 7(03): 2122–5.
  2. Shi B, Brentari D, Shakhnarovich G, Livescu K. Fingerspelling Detection in American Sign Language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021: 4166–4175.
  3. Tewari D, Srivastava SK. A visual recognition of static hand gestures in Indian sign language based on Kohonen self-organizing map algorithm. Int J Eng Adv Technol (IJEAT). 2012 Dec; 2(2): 165–70.
  4. Murthy GR, Jadon RS. A review of vision-based hand gestures recognition. International Journal of Information Technology and Knowledge Management (IJITKM). 2009 Jul; 2(2): 405–10.
  5. Garg P, Aggarwal N, Sofat S. Vision based hand gesture recognition. International Journal of Computer and Information Engineering. 2009 Jan 28; 3(1): 186–91.
  6. Karray F, Alemzadeh M, Abou Saleh J, Arab MN. Human-computer interaction: Overview on state of the art. Int J Smart Sens Intell Syst. 2017 Dec 13; 1(1): 137–159.
  7. Nicholas Renotte. (2021 Jun 18). Sign Language Detection using Action Recognition with Python: LSTM Deep Learning Model. [Online]. YouTube. Available from
  8. https://www.youtube.com/watch?v=doDUihpj6ro
  9. Hasan MM, Misra PK. Brightness factor matching for gesture recognition system using scaled normalization. Int J Comput Sci Inf Technol. 2011; 3(2): 35–46.
  10. Li X. Gesture recognition based on fuzzy c-means clustering algorithm. Department of Computer science. The University of Tennessee Knoxville; 2003.
  11. Pandey P, Jain V. Hand gesture recognition for sign language recognition: A review. International Journal of Science, Engineering and Technology Research (IJSETR). 2015 Mar; 4(3): 464–470.
  12. Wikipedia. Tensorflow. [Online]. Available from https://en.wikipedia.org/wiki/TensorFlow
  13. GitHub. Action detection for sign language. [Online]. Available from https://github.com/nicknochnack/ActionDetectionforSignLanguage

Read more of it

Subscribe to one journal or build your own list of several. One invoice, one payment, access for a full year.

Subscribe to journals →

Publish your article

Peer-reviewed journals across every subject, with open access options. Send your manuscript to the right editor today.

Publish with us → Calculate your APC
Support