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A Real-Time Automatic Translation of Text to Sign Language
Authors:Muhammad Sanaullah  Babar Ahmad  Muhammad Kashif  Tauqeer Safdar  Mehdi Hassan  Mohd Hilmi Hasan  Norshakirah Aziz
Affiliation:1.Department of Computer Science, Bahauddin Zakariya University, Multan, 60,000, Pakistan2 Department of Computer Science, Air University, Multan, 60,000, Pakistan3 Department of Computer Science, Air University, Islamabad, 44,000, Pakistan4 Centre for Research in Data Science, Department of Computer and Information Sciences, UniversitiTeknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia
Abstract:Communication is a basic need of every human being; by this, they can learn, express their feelings and exchange their ideas, but deaf people cannot listen and speak. For communication, they use various hands gestures, also known as Sign Language (SL), which they learn from special schools. As normal people have not taken SL classes; therefore, they are unable to perform signs of daily routine sentences (e.g., what are the specifications of this mobile phone?). A technological solution can facilitate in overcoming this communication gap by which normal people can communicate with deaf people. This paper presents an architecture for an application named Sign4PSL that translates the sentences to Pakistan Sign Language (PSL) for deaf people with visual representation using virtual signing character. This research aims to develop a generic independent application that is lightweight and reusable on any platform, including web and mobile, with an ability to perform offline text translation. The Sign4PSL relies on a knowledge base that stores both corpus of PSL Words and their coded form in the notation system. Sign4PSL takes English language text as an input, performs the translation to PSL through sign language notation and displays gestures to the user using virtual character. The system is tested on deaf students at a special school. The results have shown that the students were able to understand the story presented to them appropriately.
Keywords:Sign language  sign markup language  deaf communication  hamburg notations  machine translation
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