Sign Language Digit Recognition Using Different Convolutional Neural Network Model

Main Article Content

Md. Bipul Hossain
Apurba Adhikary
Sultana Jahan Soheli

Abstract

An enormous number of world populations in current time are unique in that sense that they have no broad language because of the absence of their hearing capability. The people with hearing impairment have their own language called Sign Language however it is hard for understanding to general individuals [1]. Sign digits are additionally a significant piece of gesture based communication. So a machine interpreter is important to permit them to speak with general individuals. For making their language justifiable to general individual’s computer vision based arrangements are notable these days. In this exploration of work we target to develop a model based on CNN to deal with the recognition of Sign Language digits. A dataset of 10 classes is used to train (70%), validation (20%) and test (10%) of the network. We consider three different models of CNN network to train and test the accuracy of sign digit. Among the three model transfer learning based pre-trained CNN performs better with test accuracy of 92%.

Keywords:
Sign language, convolutional neural network, transfer learning, digit recognition.

Article Details

How to Cite
Hossain, M. B., Adhikary, A., & Soheli, S. J. (2020). Sign Language Digit Recognition Using Different Convolutional Neural Network Model. Asian Journal of Research in Computer Science, 6(2), 16-24. https://doi.org/10.9734/ajrcos/2020/v6i230154
Section
Original Research Article

References

Yohanssen Pratama, et. al. Deep convolutional neural network for hand sign language recognition using model E. Bulletin of Electrical Engineering and Informatics. 2020;9(5):1873-1881.

DOI: 10.11591/eei.v9i5.2027

Er-Rady A, Faizi R, Thami ROH, Housni H. Automatic sign language recognition: A survey. International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Fez. 2017;1-7.

DOI: 10.1109/ATSIP.2017.8075561

Rao GA, Syamala K, Kishore PVV, Sastry ASCS, Deep convolutional neural networks for sign language recognition. Conference on signal processing and communication engineering systems (spaces), Vijayawada. 2018;194-197.

DOI: 10.1109/spaces.2018.8316344

Wenjin Tao, et. al. American sign language alphabet recognition using convolutional neural networks with multiview augmentation and inference fusion; 2018.

DOI : 10.1016/J.ENGAPPAI.2018.09.006

Hossain MB, Naznin F, Joarder YA. Zahidul Islam M, Uddin MJ, Recognition and solution for handwritten equation using convolutionalneural network. Joint 7th international conference on informatics, electronics & vision (ICIEV) and 2018 2nd international conference on imaging, vision & pattern recognition (icIVPR), Kitakyushu, Japan. 2018;250-255.

DOI: 10.1109/ICIEV.2018.8640991

Stutz D, Beyer L, Understanding convolutional neural networks; 2014.

Deora D, Bajaj N, Indian sign language recognition. 2012 1st International conference on emerging technology trends in electronics, communication & networking, Surat, Gujarat, India. 2012;1-5.

Agarwal A, Thakur MK, Sign language recognition using Microsoft Kinect. Sixth International Conference on Contemporary Computing (IC3), Noida. 2013;181-185.

Pigou L, Dieleman S, Kindermans PJ, Schrauwen B. Sign language recognitionusing convolutional neural networks. In: Agapito L, Bronstein MM, Rother C. (eds.) ECCV 2014. LNCS. Springer, Cham. 20158925:572–578.

DOI: 10.1007/978-3-319-16178-540

Oyewole, Ogunsanwo Gbenga, et al. Bridging communication gap among people with hearing impairment: An application of image processing and artificial neural network. International Journal of Information and Communication Sciences. 20183(1):11.

Albawi S, Mohammed TA, Al-Zawi S, Understanding of a convolutional neural network. International Conference on Engineering and Technology (ICET), Antalya. 2017;1-6.

DOI:10.1109/ICEngTechnol.2017.8308186

Chuanqi Tan, Fuchun Sun, et. al. A survey on deep transfer learning. The 27th International Conference on Artificial Neural Networks (ICANN). arXiv: 1808.01974; 2018.

Pan SJ, Yang Q. A survey on transfer learning. in IEEE Transactions on Knowledge and Data Engineering. 2010; 22(10)1345-1359.

DOI: 10.1109/TKDE.2009.191.

Fahim Sikder M. Bangla handwritten digit recognition and generation. In: uddin m., bansal j. (eds) proceedings of international joint conference on computational intelligence. Algorithms for intelligent systems. Springer, Singapore; 2020.

Available:https://www.kaggle.com/ardamavi/sign-language-digits-dataset

[Accessed date:01-05-20]