The Development of Hand Gestures Recognition Research: A Review

(1) Achmad Noer Aziz Mail (Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia)
(2) * Arrie Kurniawardhani Mail (Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia)
*corresponding author


This paper contains a review of the literature published in the last 5 years that discusses the topic of hand gesture recognition. The focus in this paper leads the reader to see the development of research over the years in hand gesture recognition, in particular that discusses about performance, methods, and datasets used in hand gesture recognition. From this paper, hopefully it can attract researchers’ interest to develop technology more deeply, especially in the field of hand gesture recognition. Hand gestures are not only used as a medium of communication for people with disabilities. Hand gestures can also be used to interact with a computer without any special devices with the technology that is available today.


Disabilities, Hand Gesture Recognition, Human-Computer Interaction, Machine Learning



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A. Harpini, “Disabilitas Rungu,” 2019. [Online]. Available: [Accessed: 03-May-2020].

K. Bantupalli and Y. Xie, “American Sign Language Recognition using Deep Learning and Computer Vision,” in 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 4896–4899.

Suharjito, N. Thiracitta, H. Gunawan, and G. Witjaksono, “The Comparison of Some Hidden Markov Models for Sign Language Recognition,” in 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), 2018, pp. 6–10.

A. Zanzarukiya, B. Jethwa, M. Panchasara, and R. Parekh, “Assistive Hand Gesture Glove for Hearing and Speech Impaired,” in 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020, pp. 837–841.

N. Alnaim, M. Abbod, and A. Albar, “Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke,” in 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2019, pp. 1–6.

S. Sharma, S. Jain, and Khushboo, “A Static Hand Gesture and Face Recognition System for Blind People,” in 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019, pp. 534–539.

M. Fei, Z. Ju, X. Zhen, and J. Li, “Real-time visual tracking based on improved perceptual hashing,” Multimed. Tools Appl., vol. 76, no. 3, pp. 4617–4634, Feb. 2017.

G. Li et al., “Hand gesture recognition based on convolution neural network,” Cluster Comput., vol. 22, pp. 2719–2729, 2019.

R. A. Elsayed, M. S. Sayed, and M. I. Abdalla, “Hand gesture recognition based on dimensionality reduction of histogram of oriented gradients,” 2017 Proc. Japan-Africa Conf. Electron. Commun. Comput. JAC-ECC 2017, vol. 2018-Janua, no. 1, pp. 119–122, 2018.

F. A. Mufarroha and F. Utaminingrum, “Hand gesture recognition using adaptive network based fuzzy inference system and K-nearest neighbor,” Int. J. Technol., 2017.

O. K. Oyedotun and A. Khashman, “Deep learning in vision-based static hand gesture recognition,” Neural Comput. Appl., vol. 28, no. 12, pp. 3941–3951, 2017.

Y. H. Sharath Kumar and V. Vinutha, “Hand gesture recognition for sign language: A skeleton approach,” Adv. Intell. Syst. Comput., vol. 404, no. May, pp. 611–623, 2016.

T. Mantecón, C. R. del-Blanco, F. Jaureguizar, and N. García, “A real-time gesture recognition system using near-infrared imagery,” PLoS One, vol. 14, no. 10, pp. 1–17, 2019.

N. Yuliana, K. Ratri, and R. Wardani, “Metode Convex Hull dan Convexity Defects untuk Pengenalan Isyarat Tangan,” J. Telemat., vol. 11, no. 2, pp. 81–88, 2016.

A. A. Gafar and J. Y. Sari, “Sistem Pengenalan Bahasa Isyarat Indonesia dengan Menggunakan Metode Fuzzy K-Nearest Neighbor,” J. Ultim., vol. 9, no. 2, pp. 122–128, 2018.

A. Rozani, “PENGENALAN BAHASA ISYARAT ABJAD JARI,” vol. 1, no. 1, pp. 311–317, 2017.

R. Parlindungan and M. Rizaldi, “Identifikasi dan Klasifikasi Gestur Tangan dengan Sinyal EMG Hand Gesture Identification and Classification based on EMG Signals,” no. November 2019, pp. 242–249, 2019.

M. Bagus, S. Bakti, and Y. M. Pranoto, “Pengenalan Angka Sistem Isyarat Bahasa Indonesia Dengan Menggunakan Metode Convolutional Neural Network,” Semin. Nas. Inov. Teknol., pp. 11–16, 2019.

R. Z. Fadillah, “Model Penerjemah Bahasa Isyarat Indonesia ( Bisindo ) Menggunakan Convolutional Neural Network Model Penerjemah Bahasa Isyarat Indonesia ( Bisindo ) Menggunakan Convolutional Neural Network,” Fak. Sains dan Ilmu Komputer, Progr. Stud. Ilmu Komputer, Univ. Pertamina, 2020.

D. Yolanda, K. Gunadi, and E. Setyati, “Pengenalan Alfabet Bahasa Isyarat Tangan Secara Real- Time dengan Menggunakan Metode Convolutional Neural Network dan Recurrent Neural Network,” Infra, vol. 8, no. 1, 2020.

V. Adithya and R. Rajesh, “A Deep Convolutional Neural Network Approach for Static Hand Gesture Recognition,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 2353–2361, 2020.

Y. Pratama, E. Marbun, Y. Parapat, and A. Manullang, “Deep convolutional neural network for hand sign language recognition using model e,” Bull. Electr. Eng. Informatics, vol. 9, no. 5, pp. 1873–1881, 2020.

Q. Cui, Z. Zhou, C. Yuan, X. Sun, and Q. M. Jonathan Wu, “Fast American Sign Language Image Recognition Using CNNs with Fine-tuning,” J. Internet Technol., vol. 19, no. 7, pp. 2207–2214, 2018.

M. E. M. Cayamcela and W. Lim, “Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time,” 2019 Int. Conf. Comput. Netw. Commun. ICNC 2019, pp. 100–104, 2019.

M. R. Islam, U. K. Mitu, R. A. Bhuiyan, and J. Shin, “Hand gesture feature extraction using deep convolutional neural network for recognizing American sign language,” 2018 4th Int. Conf. Front. Signal Process. ICFSP 2018, pp. 115–119, 2018.

R. Patel, J. Dhakad, K. Desai, T. Gupta, and S. Correia, “Hand Gesture Recognition System using Convolutional Neural Networks,” in 2018 4th International Conference on Computing Communication and Automation (ICCCA), 2018, pp. 1–6.

F. Zhan, “Hand gesture recognition with convolution neural networks,” Proc. - 2019 IEEE 20th Int. Conf. Inf. Reuse Integr. Data Sci. IRI 2019, pp. 295–298, 2019.

“Sistem Informasi Manajemen Penyandang Disabilitas Kementerian Sosial.” [Online]. Available: [Accessed: 05-May-2020].

KLOBILITY, “BISINDO dan SIBI: Apa Bedanya?” [Online]. Available: [Accessed: 14-Jul-2020].

P. P. Ippolito, “Hyperparameters Optimization.” [Online]. Available: [Accessed: 21-Nov-2020].

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