Open Access Open Access  Restricted Access Subscription Access

Deep Learning-Based Sign Language Recognition System

Aakash Shaji, Shan S, Roshan M Shafeek, Athul A, Smitha C S

Abstract


The area of hand gesture recognition has many interesting applications. An important application of hand gesture recognition is the translation of sign language, which is a complex structured form of hand movements.  A rigorous experimental evaluation of computer vision-based methods for sign language recognition is the goal of this research article. To understand sign language recognition, the current study focuses on mapping non-segmented video streams to glosses. Long Short-Term Memory (LSTM) is one of the Recurrent Neural Network (RNN) layers that make up the proposed machine learning model. Current Deep learning frameworks, such as Google Tensor Flow and Keras API, are used to implement the model.


Full Text:

PDF

References


Improving Human Body Part Detection using Deep Learning and Motion Consistency, 2016 14th International Conference on Control, Automation, Robotics & Vision Phuket, Thailand, 13-15th November 2016 (ICARCV 2016), Manoj Ramanathan School of Electrical and Electronics Engineering, Nanyang Technological University Singapore.

Lean Karlo S. Tolentino, Ronnie O. Serfa Juan” Static Sign Language Recognition Using Deep Learning” International Journal of Machine Learning and Computing, Vol. 9, No. 6, December 2019.

Pavlo Molchanov, Shalini Gupta” Hand Gesture Recognition with 3D Convolutional Neural Networks”2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Muthu Mariappan, H., & Gomathi, V., “Real-Time Recognition of Indian Sign Language,” 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 2019

T. Starner, J. Weaver, ‘‘Real-time American sign language recognition using desk and wearable computer based video,’’ IEEE Trans. Pattern Anal. Mach. Intell., Dec. 1998

A. Mohanty, S. S. Rambhatla, and R. R. Sahay, ‘‘Deep gesture: Static hand gesture recognition using CNN,’’ in Proc. Comput. Vis. Image Process., Roorkee, India, Sep. 2017

D. Kelly, J. McDonald, and C. Markham, ‘‘A person independent system for recognition of hand postures used in sign language,’’ Pattern Recognit. Lett. Aug. 2010.

S. Aly and S. Mohammed, ‘‘Arabic sign language recognition using spatiotemporal local binary patterns and support vector machine,’’ in Proc. Int. Conf. Adv. Mach. Learn. Technol. Appl., Cairo, Egypt, Nov. 2014

P. Molchanov, S. Gupta, K. Kim, and J. Kautz, ‘‘Hand gesture recognition with 3D convolutional neural networks,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Boston, MA, USA, Jun. 2015, pp. 1–7

S. Kausar and M. Y. Javed, ‘‘A survey on sign language recognition,’’ in Proc. Frontiers Inf. Technol., Islamabad, Pakistan, Dec. 2011


Refbacks

  • There are currently no refbacks.