Kolivand, H, Joudaki, S, Sunar, MS and Tully, D (2021) An Implementation of Sign Language Alphabet Hand Posture Recognition using Geometrical Features through Artificial Neural Network (Part 2). Neural Computing and Applications, 33. pp. 13885-13907. ISSN 0941-0643

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Abstract

In the sign language alphabet, several hand signs are in use. Automatic recognition of performed hand signs can facilitate the communication between hearing and none hearing people. This framework proposes hand posture recognition of the American Sign Language alphabet based on a Neural Network (NN) which works on geometrical feature extraction of the hand. The user’s hand is captured by a 3D depth-based sensor camera. Consequently, the hand is segmented according to the depth features. The proposed system is called “Depth-based Geometrical Sign Language Recognition” (DGSLR). The DGSLR adopted an easier hand segmentation approach, which is further used in other segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation or rotation compared to Discrete Cosine Transform (DCT) and Moment Invariant. As a Support Vector Machine (SVM) is a type of Artificial Neural Network (ANN), it is used to drive desired outcomes. Since there are 26 different signs in the Sign Language alphabet, a multiclass SVM versus a single SVM classifier with 26 classes by an RBF kernel was used to validate each class. The proposed framework is proficient to hand posture recognition and provides an accuracy of up to 96.78 %. The findings of the iterations demonstrated that the combination of the extracted features resulted in a better accuracy rate in the recognition process in the classification step.

Item Type:	Article
Additional Information:	This is a post-peer-review, pre-copyedit version of an article published in . Neural Computing and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-021-06025-3
Uncontrolled Keywords:	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	Springer
Date Deposited:	08 Apr 2021 08:33
Last Modified:	22 Apr 2022 00:50
DOI or ID number:	10.1007/s00521-021-06025-3
URI:	https://researchonline.ljmu.ac.uk/id/eprint/14761

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