Abstract
This paper proposes a model based on convolutional neural network for hand gesture recognition and classification. The dataset uses 26 different hand gestures, which map to English alphabets A–Z. Standard dataset called Hand Gesture Recognition available in Kaggle website has been considered in this paper. The dataset contains 27,455 images (size 28 * 28) of hand gestures made by different people. Deep learning technique is used based on CNN which automatically learns and extracts features for classifying each gesture. The paper does comparative study with four recent works. The proposed model reports 99% test accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Premaratne P (2014) Human Computer Interaction Using Hand Gestures, vol XV, pp 158–174. ISBN 978-981-4585-68-2
Hiddinga A, Crasborn O (2011) Signed languages and globalization. Lang Soc 40(4):483–505
Dong C (2015) American sign language alphabet recognition using Microsoft Kinect. Masters Thesis, 7392. https://scholarsmine.mst.edu/masters_theses/7392
Pigou L, Dieleman S, Kindermans P-J, Schrauwen B Sign language recognition using convolutional neural networks. In: European Conference on Computer Vision (ECCV 2014) Workshops. Springer, pp 572–578
Vishwas S, Hemanth Gowda M, Vivek Chandra HN Tannvi (2018) Sign language translator using machine learning. Int J Appl Eng Res 13(4):1–5 ISSN 0973-4562
Huang J, Zhou W, Zhang Q, Li H, Li W (2018) Video-based sign language recognition without temporal segmentation. In: 32nd AAAI Conference on Artificial Intelligence (AAAI-2018), New Orleans, Louisiana, USA, 2–7 February 2018. arXiv:1801.10111
Suarez J, Murphy RR (2012) Hand gesture recognition with depth images: a review. In: 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, 9–13 September 2012
Dia J, Patil D, Raut P, Lopes M (2019) Sign language interpretation using deep learning. IOSR J Eng (IOSRJEN) 09(4):17–25 ISSN (e) 2250-3021, ISSN (p) 2278-8719
Das A, Gawde S, Suratwala K, Kalbande D (2018) Sign language recognition using deep learning on custom processed static gesture images. In: 2018 International Conference on Smart City and Emerging Technology (ICSCET), 5 January 2018. ISBN 978-1-5386-1185-2
Ji Y, Kim S, Lee K-B (2017) Sign language learning system with image sampling and convolutional neural network. In: 2017 First IEEE International Conference on Robotic Computing, Taiwan, 10–12 April 2017. ISBN 978-1-5090-6724-4
Huang J, Zhou W, Li H, Li W (2015) Sign language interpretation using deep learning. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), Italy, 29 June–3 July 2015. ISBN 978-1-4799-7082-7
Nagi J., Ducatelle F, Di Caro GA, Cireşan D, Meier U, Giusti A, Nagi F, Schmidhuber J, Gambardella LM (2011) Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011), Malaysia, 16–18 November 2011. ISBN 978-1-4577-0242-6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Goswami, T., Javaji, S.R. (2021). CNN Model for American Sign Language Recognition. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-7961-5_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7960-8
Online ISBN: 978-981-15-7961-5
eBook Packages: EngineeringEngineering (R0)