Software Implementation of Gesture Recognition Algorithm Using Computer Vision

: сс. 21 - 26
Lviv Polytechnic National University
Lviv Polytechnic National University

This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed. The modules include the implementation of perception, transformation and image processing, the creation of a neural network using artificial intelligence tools to train a model for predicting input gesture labels. The aim of this work is to create a full-fledged program for implementing a real-time gesture recognition algorithm using computer vision and machine learning.

  1. Stenger, B., Thayananthan, A., Torr, P. and Cipolla, R., (2006). Model-based hand tracking using a hierarchical Bayesian filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9), pp.1372-1384.
  2. Wang, H., Chai, X. and Chen, X., (2016). Sparse Observation (SO) Alignment for Sign Language Recognition. Neurocomputing, 175, pp.674-685.
  3. Wang, Q., Chen, X., Zhang, L., Wang, C. and Gao, W., (2007). Viewpoint invariant sign language recognition. Computer Vision and Image Understanding, 108(1-2).
  4. Nixon, M. and Aguado, A., (2019). Feature extraction and image processing for computer vision. 4th ed. New York: Academic Press, p.650.
  5. Barghout, L., (2016). Image Segmentation Using Fuzzy Spatial-Taxon Cut: Comparison of Two Different Stage One Perception Based Input Models of Color (Bayesian Classifier and Fuzzy Constraint). Electronic Imaging, 2016(16), pp.1-6.
  6. Zhang, Y. and Wu, L., (2011). Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy, 13(4), pp.841-859.
  7. Lai, Y. and Rosin, P., (2014). Efficient Circular Thresholding. IEEE Transactions on Image Processing, 23(3), pp.992-1001.
  8. Brinkmann, R., (1999). The Art and science of digital compositing. San Diego, Calif.: Morgan Kaufmann, p.184.
  9. Shapiro, L. and Stockman, G., (2001). Computer vision. Upper Saddle River, NJ: Prentice Hall, pp.137,150.
  10. Morris, T., (2004). Computer vision and image processing. Basingstoke: Palgrave Macmillan.
  11. Vandoni, C. and Huang, T., (1996). Proceedings / 1996 CERN School of Computing. Geneva: CERN.
  12. Schmidhuber, J., (2015). Deep learning in neural networks: An overview. Neural Networks, 61, pp.85-117.
  13. Bengio, Y., (2009). Learning Deep Architectures for AI. Foundations and Trends<sup class="reg">®</sup> in Machine Learning, 2(1), pp.1-127.
  14. Cireşan, D., Meier, U., Masci, J. and Schmidhuber, J., (2012). Multi-column deep neural network for traffic sign classification. Neural Networks, 32, pp.333-338.
  15. Capellman, J., (2020). Hands-On Machine Learning with ML.NET. [S.l.]: Packt Publishing.
  16. Esposito, D. and Esposito, F., (2020). Introducing Machine Learning. 1st ed. Microsoft Press, p.256.
  17. Asthana, A., (2021). Introducing ML.NET: Cross- platform, Proven and Open Source Machine Learning Framework | .NET Blog. [online] Available at: cross-platform-proven-and-open-source-machine-learning- framework/ .
  18. Hamill, P., (2009). Unit Test Frameworks for High- Quality Software Development. Sebastopol: O’Reilly Media, Inc.
  19. 2021. American Sign Language Translator (ASL) — LingoJam. [online] Available at:
  20. (2021). TechCrunch is now a part of Verizon Media. [online] Available at: app-that-understands-sign-language/.