Abstract

The ability to bridge the communication gap between people with speech and hearing impairments is a benefit of sign language. Gestures and postures are combined to create sign language. The natural method of communication is gestures, which are signs made with or without an object. People with disabilities could communicate their feelings without an interpreter, thanks to the sign language system. A novel webcam-based hand gesture recognition system for two-digit classification is proposed in this paper. This system's basic concept is to convert gestures from digital images or devices that capture images into text. Due to individual variations in hand colour, size, and shape, gesture-based communication is very complicated. The data glove-based method and hand-attached motion sensors are two examples of various approaches to gesture recognition. The hand's position and movement are tracked by these sensors. An appropriate set of equipment is required for the sensor-based method. It is expensive and may impede the hand's natural movement. A rapid and dependable vision-based method should be developed to overcome the limitations of data glove-relied approaches. The proposed system utilized the live feed of image processing and machine learning's learning capabilities to solve the recognition problem.

References

1. S. R. Kalbhor and A. M. Deshpande, (2018), Digit Recognition Using Machine Learning and Convolutional Neural Network, 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 604-609, 2018.
2. J. Sun, T. Ji, S. Zhang, J. Yang and G. Ji, (2018), Research on the Hand Gesture Recognition Based on Deep Learning, 2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Hangzhou, pp. 1-4, 2018
3. H. Chidananda and T. Hanumantha Reddy, (2017), Recognition of human hand activity to convey series of numerical digits in a natural way using hand shape tree structure, 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1-6, 2017
4. B. Feng et al., (2017), Depth-Projection-Map-Based Bag of Contour Fragments for Robust Hand Gesture Recognition, IEEE Transactions on Human-Machine Systems, vol. 47, no. 4, pp. 511-523, 2017
5. S. Poularakis and I. Katsavounidis, (2016), Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters, IEEE Transactions on Cybernetics, vol. 46, no. 9, pp. 2094-2108, 2016
6. Y. Yao and C. Li, (2015), Hand gesture recognition and spotting in uncontrolled environments based on classifier weighting, 2015 IEEE International Conference on Image Processing (ICIP), pp. 3082-3086, 2015
7. O. De, P. Deb, S. Mukherjee, S. Nandy, T. Chakraborty and S. Saha, (2016), Computer vision based framework for digit recognition by hand gesture analysis, 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1-5, 2016
8. R. Rokade-Shinde and J. Sonawane, (2016), Dynamic hand gesture recognition, 2016 IEEE International Conference on Signal and Information Processing (IConSIP), Vishnupuri, pp. 1-4, 2016
9. Z. Yang, Y. Li, W. Chen and Y. Zheng, (2012), Dynamic hand gesture recognition using hidden Markov models, 2012 7th International Conference on Computer Science & Education (ICCSE), Melbourne, VIC, 2012, pp. 360-365.
10. M. I. N. P. Munasinghe, (2018), Dynamic Hand Gesture Recognition Using Computer Vision and Neural Networks, 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, 2018, pp. 1-5.

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