Repository of Static and Dynamic Signs

Saqib, Shazia; Kazmi, Syed Asad Raza

doi:10.14569/ijacsa.2017.081113

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Sign language is a way of communication among deaf communities. The sign language consists of dynamic or static hand configuration [2] , [3] . Sign alphabets are based on static hand poses which represent the letters of the alphabets and it uses gestures besides verbal communication [11] , [12] , [13] , [14] .…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset of Pakistan Sign Language and Automatic Recognition of Hand Configuration of Urdu Alphabet through Machine Learning

Imran¹,

Razzaq²,

Baig

et al. 2021

Data in Brief

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Social correspondence is one of the most significant columns that the public dependent on. Notably, language is the best way to communicate and associate with one another both verbally and nonverbally. There is a persistent communication gap among deaf and non-deaf communities because non-deaf people have less understanding of sign languages. Every region/country has its sign language. In Pakistan, the sign language of Urdu is a visual gesture language that is being used for communication among deaf peoples. However, the dataset of Pakistan Sign Language (PSL) is not available publicly. The dataset of PSL has been generated by acquiring images of different hand configurations through a webcam. In this work, 40 images of each hand configuration with multiple orientations have been captured. In addition, we developed, an interactive android mobile application based on machine learning that minimized the communication barrier between the deaf and non-deaf communities by using the PSL dataset. The android application recognizes the Urdu alphabet from input hand configuration.

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Section: Data Descriptionmentioning

confidence: 99%

Dataset of Pakistan Sign Language and Automatic Recognition of Hand Configuration of Urdu Alphabet through Machine Learning

Imran¹,

Razzaq²,

Baig

et al. 2021

Data in Brief

View full text Add to dashboard Cite

show abstract

“…It consists of a set of gestures and body languages to denote different meanings [1] . There are usually two types of SL gestures: static and dynamic for the hands, body and face [2]. Studying the SL is so valuable as the number of people with speech and hearing impairments has been increased according to the World Health Organization (WHO) and reached 432 million until the year of 2021, among which around 34 million cases are children and the remaining are adults [3].…”

Section: Introductionmentioning

confidence: 99%

Interpreting arabic sign alphabet by using the deep learning

Al-Obaidi,

Al-Nima,

Han

2023

INTELLIGENT BIOTECHNOLOGIES OF NATURAL AND SYNTHETIC BIOLOGICALLY ACTIVE SUBSTANCES: XIV Narochanskie Readings

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Sign Language (SL) is a communication method between people. It is an essential language; especially for people who are speech impaired and hearing impaired, it can be considered as their mother tongues. Hand gestures form the nonverbal communication of this language. We focus on interpreting Arabic Sign Alphabet (ASA) in this study and, as a case study, the recognition of alphabet in Iraqi Sign Language (IrSL) is carried out with the help of specialists from the "Al-Amal Institute for the Deaf and Dumb". A new ASA dataset of various hand gestures was created and adopted. In addition, a deep learning model named the Deep Arabic Sign Alphabet (DASA) is proposed, which is a developed version of the Convolutional Neural Network (CNN). It can efficiently interpret the ASA, achieving a high interpretation accuracy of 95.25%.

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“…Sign language has two types of gestures: static gestures and dynamic gestures [2]. Due to Arabic language variations (delicates), Arabic Sign Language has multiple country variants and dialects.…”

Section: Introductionmentioning

confidence: 99%

Sign Language Semantic Translation System using Ontology and Deep Learning

Elsayed¹,

Fathy²

2020

IJACSA

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Translation and understanding sign language may be difficult for some. Therefore, this paper proposes a solution to this problem by providing an Arabic sign language translation system using ontology and deep learning techniques. That is to interpret user's signs to different meanings. This paper implemented ontology on the sign language domain to solve some sign language challenges. In this first version, simple static signs composed of Arabic alphabets and some Arabic words started to translate. Deep Convolution Neural Network (CNN) architecture was trained and tested on a pre-made Arabic sign language dataset and on a dataset collected in this paper to obtain better accuracy in recognition. Experimental results show that according to the pre-made Arabic sign language dataset the classification accuracy of the training set (80% of the dataset) was 98.06% and recognition accuracy of the testing set (20% of the dataset) was 88.87%. According to the collected dataset, the classification accuracy of the training set was 98.6% and Semantic recognition accuracy of the testing set was 94.31%.

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Repository of Static and Dynamic Signs

Cited by 5 publications

References 6 publications

Dataset of Pakistan Sign Language and Automatic Recognition of Hand Configuration of Urdu Alphabet through Machine Learning

Dataset of Pakistan Sign Language and Automatic Recognition of Hand Configuration of Urdu Alphabet through Machine Learning

Interpreting arabic sign alphabet by using the deep learning

Sign Language Semantic Translation System using Ontology and Deep Learning

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