Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks

Maraqa, M.; Abu-Zaiter, R.

doi:10.4236/jilsa.2012.41004

Cited by 67 publications

(42 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The training algorithm selected for the Elman RNN was the Backpropagation [15]. This algorithm did not converge in a way that the classification rate can be trustful.…”

Section: Methodological Protocolmentioning

confidence: 99%

Convexity local contour sequences for gesture recognition

Barros

Nestor

Bisneto

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

Algorithms for hand feature extraction used in gesture recognition systems have some problems such as unnecessary information gathering. This paper proposes a novel method for feature extraction in gesture recognition systems based on the Local Contour Sequence (LCS). It is called the Convexity Local Contour Sequence (CLCS) and represents the hand shape only with the most significant information. This generates a smaller output result, but capable to model an entire dynamic gesture. It is used to classify dynamic gestures with an Elman Recurrent Network and Hidden Markov Model and presents a better result compared to regular LCS.

show abstract

“…The training algorithm selected for the Elman RNN was the Backpropagation [15]. This algorithm did not converge in a way that the classification rate can be trustful.…”

Section: Methodological Protocolmentioning

confidence: 99%

Convexity local contour sequences for gesture recognition

Barros

Nestor

Bisneto

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

show abstract

“…In In [3] Manar maraqa, Farid Al-Zboun et.al. The objective of this research is to introduce the use of different types of neural networks in human hand gesture recognition for static images as well as for dynamic gestures.…”

Section: Prior and Related Workmentioning

confidence: 95%

Mobile Translation System from Speech Language to Hand Motion Language

Rekha

Latha²

2014

2014 International Conference on Intelligent Computing Applications

View full text Add to dashboard Cite

To access information anywhere, advanced technology is needed. Nowadays mobile devices and applications are developing rapidly. Deaf and hard of hearing people find difficult to use mobile devices since they are not able to access information anywhere due to lack of services. They have difficulties with reading and writing, to read and understand all information to use mobile devices due to their hearing impairment which is an invisible disability. The gain of experience collected by deaf children in four years is equivalent to the gain of one year for hearing children. All visual textual information is not accessible for this category of people with disabilities. To provide benefits for deaf people to improve their social integration and communication. In this paper, we propose a solution to this problem by a mobile based sign language translation device for automatic translation of Indian English speech language to sign language. This helps the hearing impaired people to communicate easily with the hearing people. We focus on the relationship between deaf people and the technological progress.

show abstract

“…A supervised feed-forward neural network based training and back propagation algorithm is used for classifying hand gestures such as hand pointing up, down, left, right etc. For the sign recognition [30] uses Elmanpsilas model on the basis of neural networks and got accuracy rate of 95% for Arabic signs. An artificial neural networks models based on distance with neural-fuzzy models approach proposed in [31] for the recognition of Brazilian Sign Language recognition.…”

Section: Common Approchesmentioning

confidence: 99%

A comprehensive review on automation of Indian sign language

Verma

Srivastava

Kumar³

2015

2015 International Conference on Advances in Computer Engineering and Applications

View full text Add to dashboard Cite

Hearing impaired people uses signs to communicate with others. Just like verbally spoken languages, there is no universal language as every country has its own spoken language so every country has their own dialect of sign language and in India they uses Indian Sign Language (ISL). In the last few years, researchers take interest in the automation of ISL. Some attempts have been made in India and other countries. In this study we try to explore and analyze the work have been made with automation of sign language and gesture recognition. We tried to explore the challenges comes in the real time sign recognition system. This review also includes the progress of standard corpus creation of the ISL.

show abstract

Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks

Cited by 67 publications

References 16 publications

Convexity local contour sequences for gesture recognition

Convexity local contour sequences for gesture recognition

Mobile Translation System from Speech Language to Hand Motion Language

A comprehensive review on automation of Indian sign language

Contact Info

Product

Resources

About