Utilizing ML for Hand Gesture Recognition


  • Vaidish Srivastava Student, Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
  • Srajan Shukla Student, Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
  • Radhey Shyam Professor & Head, Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India


OpenCv, Tensor flow, Machine Learning, Deep Learning, augmented reality, hand gesture recognition


Hand gestures are an instinctive and common method of communication during human interactions, representing a form of non-verbal expression. Gesture recognition technology aims to interpret and classify meaningful movements performed by human hands. The motivation behind its development is to revolutionize human-computer interaction, addressing drawbacks found in current systems. The study presents a novel algorithm that eliminates the requirement for image background filtering. This algorithm proves versatile, capable of recognizing various hand gestures and accurately determining the count of raised fingers. By focusing on the hand movements within the region of interest, the proposed system enhances the efficiency and precision of gesture recognition. Gesture recognition has extensive applications, spanning from virtual and augmented reality to healthcare and industrial automation. Through this technology, users can engage more naturally and intuitively with digital environments, while healthcare professionals can provide real-time feedback during rehabilitation exercises. In industrial settings, gesture recognition enables the control of machines and robots, enhancing productivity and reducing manual labor. Advancements in computer vision, machine learning (ML), and sensor technology have greatly improved the accuracy and effectiveness of gesture recognition systems. With further research and development, gesture recognition is poised to revolutionize human-computer interactions across diverse fields, enriching user experiences and optimizing task performance. By continuously refining algorithms and incorporating innovative techniques, gesture recognition is poised to become an integral part of future human-machine interactions.


Padmappriya S, Sumalatha K. Digital Image Processing Real-Time Application. Int J Eng Sci Invent (IJESI). 2018; 46–51.

Oikonomidis I, Kyriazis N, Argyros AA. Efficient model-based 3D tracking of hand articulations using Kinect. Proceedings of the British Machine Vision Conference. 2011; 1–11.

Mitra S, Acharya T. Gesture recognition: A survey. IEEE Trans Syst Man Cybern, Part C (Applications and Reviews). 2007; 37(3): 311–324.

Jha D, Bharti P. Survey of hand gesture recognition techniques. International Journal of Computer Science and Information Technologies (IJCSIT). 2015; 6(1): 15–20.

Villán AF. Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7. Packt Publishing Ltd; Mar 2019.

Sun JH, Ji TT, Zhang SB, Yang JK, Ji GR. Research on the Hand Gesture Recognition Based on Deep Learning. 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Hangzhou, China. 2018; 1–4.

Khan RZ, Ibraheem NA. Hand Gesture Recognition: A Literature Review. Int J Artif Intell Appl. 2012; 3(4): 161–174.

Chung HY, Chung YL, Tsai WF. An Efficient Hand Gesture Recognition System Based on Deep CNN. IEEE International Conference on Industrial Technology (ICIT), Melbourne, VIC, Australia. 2019; 853–858.

Adithya V, Rajesh R. A Deep Convolutional Neural Network Approach for Static Hand Gesture Recognition. Procedia Comput Sci. 2020; 171: 2353–2361.

Oudah M, Al-Naji A, Chahl J. Hand Gesture Recognition Based on Computer Vision: A Review of Techniques. J Imaging. 2020; 6(8): 73.

Chen ZH, Kim JT, Liang J, Zhang J, Yuan YB. Real-Time Hand Gesture Recognition Using Finger Segmentation. Sci World J. 2014; 2014: 267872(9p).

Huang H, Chong Y, Nie C, Pan S. Hand Gesture Recognition with Skin Detection and Deep Learning Method. J Phys: Conf Ser. 2019; 1213(2): 022001.

Hu B, Wang J. Deep Learning Based Hand Gesture Recognition and UAV Flight Controls. Int J Autom Comput. 2020; 17(1): 17–29.

Yang J, Tian Y. Super normal vector for activity recognition using depth sequences. IEEE Trans Pattern Anal Mach Intell. 2014; 36(4): 763–770.

Shyam R. Convolutional Neural Network and its Architectures. J Comput Technol Appl. 2021; 12(2): 6–14.

Verma S, Jaiswal V, Shyam R. Intensifying Security with Smart Video Surveillance. Recent Trends Program Lang. 2022; 9(1): 24–30.

Srivastava V, Shyam R. Enhanced Object Detection with Deep Convolutional Neural Networks. Int J All Res Educ Sci Methods (IJARESM). 2021; 9(7): 27–36.

Pandey A, Shyam R. Analysis of Road Lane Detection Using Computer Vision. International Journal of Software Computing and Testing (IJSCT). 2023; 9(1): 7–14.

Sheenu Choudhary, Dipika Arora. Reviewing sentiment analysis and opinion mining of social media. International Journal of Innovative Research in Computer and Communication Engineering. 2017; 5(9): 15137-15141.

Simran, Tandon S, Khanna S, Shyam R. Detection of Traffic Sign Using CNN. Recent Trends Parallel Comput. 2022; 9(1): 14–23.

Li H, Zhou Z, Dai Q. Real-time hand gesture recognition using Kinect sensor. J Real-Time Image Process. 2013; 8(2): 157–167.

Wei XS, Song YZ, Mac Aodha O, Wu J, Peng Y, Tang J, Yang J, Belongie S. Fine-grained image analysis with deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. Nov 2021; 44(12): 8927-8948.

Shyam R, Khanna S, Verma P, Maurya S. Assessing the Performance of DL Methods in Handwritten Digit Recognition. International Journal of Data Structure Studies (IJDSS). 2023; 1(1): 25–32.

Erol A, Bebis G, Nicolescu M. Vision-based hand pose estimation: A review. Comput Vis Image Underst. 2007; 108(1–2): 52–73.