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A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Kolivand, H, Joudaki, S, Sunar, MS and Tully, D (2020) A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1). Neural Computing and Applications. ISSN 0941-0643

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Abstract

Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people with speech impairment to communication with normal people. This framework which is called ASLNN proposes a new hand posture recognition technique for the American sign language alphabet based on the neural network which works on the geometrical feature extraction of hands. A user’s hand is captured by a three-dimensional depth-based sensor camera; consequently, the hand is segmented according to the depth analysis features. The proposed system is called depth-based geometrical sign language recognition as named DGSLR. The DGSLR adopted in easier hand segmentation approach, which is further used in segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation compared to discrete cosine transform and moment invariant. The findings of the iterations demonstrate the combination of the extracted features resulted to improved accuracy rates. Then, an artificial neural network is used to drive desired outcomes. ASLNN is proficient to hand posture recognition and provides accuracy up to 96.78% which will be discussed on the additional paper of this authors in this journal.

Item Type: Article
Uncontrolled Keywords: 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Springer
Date Deposited: 21 Aug 2020 12:45
Last Modified: 21 Aug 2020 12:45
DOI or Identification number: 10.1007/s00521-020-05279-7
URI: https://researchonline.ljmu.ac.uk/id/eprint/13530

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