NDLI: Multi-modality-based Arabic sign language recognition

Content Provider	IET Digital Library
Author	Elpeltagy, Marwa Abdelwahab, Moataz Hussein, Mohamed E. Shoukry, Amin Shoala, Asmaa Galal, Moustafa
Abstract	With the increase in the number of deaf-mute people in the Arab world and the lack of Arabic sign language (ArSL) recognition benchmark data sets, there is a pressing need for publishing a large-volume and realistic ArSL data set. This study presents such a data set, which consists of 150 isolated ArSL signs. The data set is challenging due to the great similarity among hand shapes and motions in the collected signs. Along with the data set, a sign language recognition algorithm is presented. The authors’ proposed method consists of three major stages: hand segmentation, hand shape sequence and body motion description, and sign classification. The hand shape segmentation is based on the depth and position of the hand joints. Histograms of oriented gradients and principal component analysis are applied on the segmented hand shapes to obtain the hand shape sequence descriptor. The covariance of the three-dimensional joints of the upper half of the skeleton in addition to the hand states and face properties are adopted for motion sequence description. The canonical correlation analysis and random forest classifiers are used for classification. The achieved accuracy is 55.57% over 150 ArSL signs, which is considered promising.
Starting Page	1031
Ending Page	1039
Page Count	9
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 7, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0598
Journal	IET Computer Vision
Publisher Date	2018-05-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Arabic Sign Language Recognition Benchmark Data Sets ArSL Signs Body Motion Description Canonical Correlation Analysis Computer Vision And Image Processing Technique Deaf-mute People Face Property Feature Extraction Hand Joints Hand Segmentation Hand Shape Segmentation Hand Shape Sequence Descriptor Hand State Handicapped Aids Histogram of Oriented Gradients Image Classification Image Motion Analysis Image Recognition Image Segmentation Motion Sequence Description Multimodality-based Arabic Sign Language Recognition Principal Component Analysis Random Forest Classifier Segmented Hand Shapes Sign Classification Sign Language Recognition Sign Language Recognition Algorithm Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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