Arabie sign language recognition using the Microsoft Kinect

Aliyu, Salihu O.; Mohandes, M.; Deriche, Mohamed; Badran, Saeed M.

doi:10.1109/ssd.2016.7473753

Cited by 16 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This factor measures the simultaneous firing strength for connected pixels. Aliyu et al [36] proposed a new method for Arabic sign language recognition to detect and track at least one hand and at least one finger of the at least one hand from at least two different locations in a room by two different sensors; generating a 3-dimensional (3D) interaction space. They extracted 3D features associated with the one detected and tracked hand or both hands and at least one detected and tracked finger then they analyzed a relevance metric related to the extracted 3D features.…”

Section: Related Workmentioning

confidence: 99%

“…The system performed 95% recognition accuracy for a dataset of 20 dynamic signs due to the additional modules for facial expressions recognition and body movement recognition. Aliyu et al [36] developed a Kinect based system for Arabic sign language recognition system. The developed system was tested with 20 signs from the Arabic language dictionary.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Hisham

Hamouda

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Human-Computer Interaction (HCI) refers to the interaction between the computers and human. One of the most important applications of (HCI) is sign language recognition. Several research works aimed to interpret and translate the sign language to a spoken language to help the hear impaired persons in integrating with communities. Sign language is the main way of communication for the deaf persons and hearing impaired which enable them to communicate with their societies and between each other's. According to the World Health Organization, there are 466 million hearing loss people (i.e. 5% of the world population), 432 million or 93% of them are adults and 34 million or 17% of them are children. The hearing-impaired persons often need to the same level of mental capability as the normal persons. The main problem is the most of the hearing persons who cannot understand the sign language and most of hearing impaired cannot read or write our spoken language, this represents a barrier between the deaf persons and their societies so developing an automatic sign language recognition system is very necessary. This research introduces dynamic Arabic Sign Language recognition system using Microsoft Kinect. The recognition depends on using two machine learning algorithms (a) Decision Tree and (b) Bayesian Network then applied Ada-Boosting technique to enhance the recognition of the system, we compared the results with two direct matching techniques: (a) Dynamic Time Wrapping and Hidden Markov Model the system was applied on 42 Arabic gestures related to medical field. The experimental results showed that the proposed system recognition rate reached 91.18% for Decision Tree classifier, 92.50% for Bayesian classifier and 93.7% after applying Ada-Boosting.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Hisham

Hamouda

2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…General SLTC-applications are presented, for example, by Fok et al [ 107 ] or Kumar et al [ 108 ]. Country-specific recognition approaches are available amongst others for the American [ 109 ], Australian [ 110 ], Arabic [ 111 ], Greek [ 112 ], Indian [ 113 ] and Mexican [ 114 ] sign language. An evaluation of different potential solutions for recognition, translation and representation of sign language for e-learning platforms has been conducted by Martins et al [ 115 ].…”

Section: Applications and Contextsmentioning

confidence: 99%

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Bachmann

Weichert

Rinkenauer

2018

Sensors

116

View full text Add to dashboard Cite

Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

show abstract

“…They use low level features and achieve more than 90% accuracy on 8 signs, Aliyu et al (2016) proposed an Arabic sign language recognition system based on Microsoft Kinect system. The developed system was tested with 20 signs from the Arabic sign language dictionary.…”

Section: Related Workmentioning

confidence: 99%

Arabic Static and Dynamic Gestures Recognition Using Leap Motion

Hisham¹,

Hamouda²

2017

Journal of Computer Science

View full text Add to dashboard Cite

Across the world, several millions of people use sign language as their main way of communication with their society, daily they face a lot of obstacles with their families, teachers, neighbours, employers. According to the most recent statistics of World Health Organization, there are 360 million persons in the world with disabling hearing loss i.e. (5.3% of the world's population), around 13 million in the Middle East. Hence, the development of automated systems capable of translating sign languages into words and sentences becomes a necessity. We propose a model to recognize both of static gestures like numbers, letters, ...etc and dynamic gestures which includes movement and motion in performing the signs. Additionally, we propose a segmentation method in order to segment a sequence of continuous signs in real time based on tracking the palm velocity and this is useful in translating not only pre-segmented signs but also continuous sentences. We use an affordable and compact device called Leap Motion controller, which detects and tracks the hands' and fingers' motion and position in an accurate manner. The proposed model applies several machine learning algorithms as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN) and Dynamic Time Wrapping (DTW) depending on two different features sets. This research will increase the chance for the Arabic hearing-impaired and deaf persons to communicate easily using Arabic Sign language(ArSLR). The proposed model works as an interface between hearing-impaired and normal persons who are not familiar with Arabic sign language, overcomes the gap between them and it is also valuable for social respect. The proposed model is applied on Arabic signs with 38 static gestures (28 letters, numbers (1:10) and 16 static words) and 20 dynamic gestures. Features selection process is maintained and we get two different features sets. For static gestures, KNN model dominates other models for both of palm features set and bone features set with accuracy 99 and 98% respectively. For dynamic gestures, DTW model dominates other models for both palm features set and bone features set with accuracy 97.4% and 96.4% respectively.

show abstract

Arabie sign language recognition using the Microsoft Kinect

Cited by 16 publications

References 14 publications

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Arabic Static and Dynamic Gestures Recognition Using Leap Motion

Contact Info

Product

Resources

About